Nima Fazeli

Prehensile autonomous manipulation, such as peg insertion, tool use, or assembly, require precise in-hand understanding of the object pose and the extrinsic contacts made during interactions. Providing accurate estimation of pose and contacts is challenging. Tactile sensors can provide local geometry at the sensor and force information about the grasp, but the locality of sensing means resolving poses and contacts from tactile alone is often an ill-posed problem, as multiple configurations can be consistent with the observations. Adding visual feedback can help resolve ambiguities, but can suffer from noise and occlusions. In this work, we propose a method that pairs local observations from sensing with the physical constraints of contact. We propose a set of factors that ensure local consistency with tactile observations as well as enforcing physical plausibility, namely, that the estimated pose and contacts must respect the kinematic and force constraints of quasi-static rigid body interactions. We formalize our problem as a factor graph, allowing for efficient estimation. In our experiments, we demonstrate that our method outperforms existing geometric and contact-informed estimation pipelines, especially when only tactile information is available. Video results can be found at tacgraph.github.io.

Prehensile autonomous manipulation, such as peg insertion, tool use, or assembly, require precise in-hand understanding of the object pose and the extrinsic contacts made during interactions. Providing accurate estimation of pose and contacts is challenging. Tactile sensors can provide local geometry at the sensor and force information about the grasp, but the locality of sensing means resolving poses and contacts from tactile alone is often an ill-posed problem, as multiple configurations can be consistent with the observations. Adding visual feedback can help resolve ambiguities, but can suffer from noise and occlusions. In this work, we propose a method that pairs local observations from sensing with the physical constraints of contact. We propose a set of factors that ensure local consistency with tactile observations as well as enforcing physical plausibility, namely, that the estimated pose and contacts must respect the kinematic and force constraints of quasi-static rigid body interactions. We formalize our problem as a factor graph, allowing for efficient estimation. In our experiments, we demonstrate that our method outperforms existing geometric and contact-informed estimation pipelines, especially when only tactile information is available. Video results can be found at https://tacgraph.github.io/.

Prehensile autonomous manipulation, such as peg insertion, tool use, or assembly, require precise in-hand understanding of the object pose and the extrinsic contacts made during interactions. Providing accurate estimation of pose and contacts is challenging. Tactile sensors can provide local geometry at the sensor and force information about the grasp, but the locality of sensing means resolving poses and contacts from tactile alone is often an ill-posed problem, as multiple configurations can be consistent with the observations. Adding visual feedback can help resolve ambiguities, but can suffer from noise and occlusions. In this work, we propose a method that pairs local observations from sensing with the physical constraints of contact. We propose a set of factors that ensure local consistency with tactile observations as well as enforcing physical plausibility, namely, that the estimated pose and contacts must respect the kinematic and force constraints of quasi-static rigid body interactions. We formalize our problem as a factor graph, allowing for efficient estimation. In our experiments, we demonstrate that our method outperforms existing geometric and contact-informed estimation pipelines, especially when only tactile information is available. Video results can be found at https://tacgraph.github.io/.

Imitation Learning (IL) holds great potential for learning repetitive manipulation tasks, such as those in industrial assembly. However, its effectiveness is often limited by insufficient trajectory precision due to compounding errors. In this paper, we introduce Grasped Object Manifold Projection (GrOMP), an interactive method that mitigates these errors by constraining a non-rigidly grasped object to a lower-dimensional manifold. GrOMP assumes a precise task in which a manipulator holds an object that may shift within the grasp in an observable manner and must be mated with a grounded part. Crucially, all GrOMP enhancements are learned from the same expert dataset used to train the base IL policy, and are adjusted with an n-arm bandit-based interactive component. We propose a theoretical basis for GrOMP's improvement upon the well-known compounding error bound in IL literature. We demonstrate the framework on four precise assembly tasks using tactile feedback, and note that the approach remains modality-agnostic. Data and videos are available at williamvdb.github.io/GrOMPsite.

Increasingly large datasets of robot actions and sensory observations are being collected to train ever-larger neural networks. These datasets are collected based on tasks and while these tasks may be distinct in their descriptions, many involve very similar physical action sequences (e.g., 'pick up an apple' versus 'pick up an orange'). As a result, many datasets of robotic tasks are substantially imbalanced in terms of the physical robotic actions they represent. In this work, we propose a simple sampling strategy for policy training that mitigates this imbalance. Our method requires only a few lines of code to integrate into existing codebases and improves generalization. We evaluate our method in both pre-training small models and fine-tuning large foundational models. Our results show substantial improvements on low-resource tasks compared to prior state-of-the-art methods, without degrading performance on high-resource tasks. This enables more effective use of model capacity for multi-task policies. We also further validate our approach in a real-world setup on a Franka Panda robot arm across a diverse set of tasks.

Tactile sensors have long been valued for their perceptual capabilities, offering rich insights into the otherwise hidden interface between the robot and grasped objects. Yet their inherent compliance -- a key driver of force-rich interactions -- remains underexplored. The central challenge is to capture the complex, nonlinear dynamics introduced by these passive-compliant elements. Here, we present a computationally efficient non-holonomic hydroelastic model that accurately models path-dependent contact force distributions and dynamic surface area variations. Our insight is to extend the object's state space, explicitly incorporating the distributed forces generated by the compliant sensor. Our differentiable formulation not only accounts for path-dependent behavior but also enables gradient-based trajectory optimization, seamlessly integrating with high-resolution tactile feedback. We demonstrate the effectiveness of our approach across a range of simulated and real-world experiments and highlight the importance of modeling the path dependence of sensor dynamics.

In this paper, we propose AimBot, a lightweight visual augmentation technique that provides explicit spatial cues to improve visuomotor policy learning in robotic manipulation. AimBot overlays shooting lines and scope reticles onto multi-view RGB images, offering auxiliary visual guidance that encodes the end-effector's state. The overlays are computed from depth images, camera extrinsics, and the current end-effector pose, explicitly conveying spatial relationships between the gripper and objects in the scene. AimBot incurs minimal computational overhead (less than 1 ms) and requires no changes to model architectures, as it simply replaces original RGB images with augmented counterparts. Despite its simplicity, our results show that AimBot consistently improves the performance of various visuomotor policies in both simulation and real-world settings, highlighting the benefits of spatially grounded visual feedback.

Fig. 1: ViTaSCOPE: Visuo-Tactile Simultaneous Contact and Object Pose Estimation.We present an object-centric neural implicit representation that enables simultaneous in-hand object pose and extrinsic contact estimation from vision and highresolution tactile sensing.Our method is trained entirely in simulation and zero-shot transferred to the real world.In the example above, the tool grasped by the robot makes extrinsic contact with the table.ViTaSCOPE is able to infer the in-hand object pose and register the extrinsic contact (patch highlighted in blue) to the 3D geometry from partial real-world observations.Abstract-Mastering dexterous, contact-rich object manipulation demands precise estimation of both in-hand object poses and external contact locations-tasks particularly challenging due to partial and noisy observations.We present ViTaSCOPE: Visuo-Tactile Simultaneous Contact and Object Pose Estimation, an object-centric neural implicit representation that fuses vision and high-resolution tactile feedback.By representing objects as signed distance fields and distributed tactile feedback as neural shear fields, ViTaSCOPE accurately localizes objects and registers extrinsic contacts onto their 3D geometry as contact fields.Our method enables seamless reasoning over complementary visuotactile cues by leveraging simulation for scalable training and zero-shot transfers to the real-world by bridging the sim-to-real gap.We evaluate our method through comprehensive simulated and real-world experiments, demonstrating its capabilities in dexterous manipulation scenarios.

We introduce Vib2Move, a novel approach for inhand object reconfiguration that uses fingertip micro-vibrations and gravity to precisely reposition planar objects.Our framework comprises three key innovations.First, we design a vibrationbased actuator that dynamically modulates the effective finger-object friction coefficient, effectively emulating changes in gripping force.Second, we derive a sliding motion model for objects clamped in a parallel gripper with two symmetric, variablefriction contact patches.Third, we propose a motion planner that coordinates end-effector finger trajectories and fingertip vibrations to achieve the desired object pose.In real-world trials, Vib2Move consistently yields final positioning errors below 6 mm, demonstrating reliable, high-precision manipulation across a variety of planar objects.For more results and information, please visit https://vib2move.github.io.

A transient flow model is developed to predict the flow of high-viscosity fluid dispensing for precision direct ink writing (DIW) in additive manufacturing. Models for pump deformation and fluid friction to accurately predict flow of a high-viscosity non-Newtonian fluid through a progressive cavity pump, static mixer, and a tapered nozzle are created. Inside the progressive cavity pump, the effect of elastic deformation on modeling high-viscosity fluid transient flow is included. Based on the Characteristic Method (CM) and boundary conditions for DIW, the continuity and momentum equations are numerically solved. Using deformation modeling and CM, the transient response of the DIW system to the input volumetric flow rate is modeled for both a pipe and static mixer. The transient response of the DIW output volumetric flow rate is recorded using flow and pressure sensors and found to match the flow model. The deformation and CM models are applied to predict the swelling of a 90° corner DIW tool path from trapezoidal motion planning with accelerations from 100 to 2000 mm/s 2 . Predicted corner swelling is matched with the actual corner swelling via image processing of the 90° corner. The corner swelling is significant, ranging from 0.76 to 0.37 mm for a line width of 0.25 mm and a height of 0.15 mm, and represents the model’s ability to quantify print errors. This study demonstrates that the flow model can accurately predict the transient response of the DIW volumetric flow rate, which is foundational to high-fidelity flow control and compensation in precision DIW.

Mastering dexterous, contact-rich object manipulation demands precise estimation of both in-hand object poses and external contact locations$\unicode{x2013}$tasks particularly challenging due to partial and noisy observations. We present ViTaSCOPE: Visuo-Tactile Simultaneous Contact and Object Pose Estimation, an object-centric neural implicit representation that fuses vision and high-resolution tactile feedback. By representing objects as signed distance fields and distributed tactile feedback as neural shear fields, ViTaSCOPE accurately localizes objects and registers extrinsic contacts onto their 3D geometry as contact fields. Our method enables seamless reasoning over complementary visuo-tactile cues by leveraging simulation for scalable training and zero-shot transfers to the real-world by bridging the sim-to-real gap. We evaluate our method through comprehensive simulated and real-world experiments, demonstrating its capabilities in dexterous manipulation scenarios.

We introduce Vib2Move, a novel approach for in-hand object reconfiguration that uses fingertip micro-vibrations and gravity to precisely reposition planar objects. Our framework comprises three key innovations. First, we design a vibration-based actuator that dynamically modulates the effective finger-object friction coefficient, effectively emulating changes in gripping force. Second, we derive a sliding motion model for objects clamped in a parallel gripper with two symmetric, variable-friction contact patches. Third, we propose a motion planner that coordinates end-effector finger trajectories and fingertip vibrations to achieve the desired object pose. In real-world trials, Vib2Move consistently yields final positioning errors below 6 mm, demonstrating reliable, high-precision manipulation across a variety of planar objects. For more results and information, please visit https://vib2move.github.io.

Dexterous manipulation requires careful reasoning over extrinsic contacts. The prevalence of deforming tools in human environments, the use of deformable sensors, and the increasing number of soft robots yields a need for approaches that enable dexterous manipulation through contact reasoning where not all contacts are well characterized by classical rigid body contact models. Here, we consider the case of a deforming tool dexterously manipulating a rigid object. We propose a hybrid learning and first-principles approach to the modeling of simultaneous motion and force transfer of tools and objects. The learned module is responsible for jointly estimating the rigid object's motion and the deformable tool's imparted contact forces. We then propose a Contact Quadratic Program to recover forces between the environment and object subject to quasi-static equilibrium and Coulomb friction. The results is a system capable of modeling both intrinsic and extrinsic motions, contacts, and forces during dexterous deformable manipulation. We train our method in simulation and show that our method outperforms baselines under varying block geometries and physical properties, during pushing and pivoting manipulations, and demonstrate transfer to real world interactions. Video results can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://deform-rigid-contact.github.io/</uri>.

Clear, interpretable instructions are invaluable for complex tasks, helping to clarify goals and anticipate necessary steps. In this work, we propose a robot learning framework for communicating, planning, and executing a wide range of tasks, dubbed This&That. This&That solves general tasks by leveraging video generative models, which, through training on internet-scale data, contain rich physical and semantic context. Through this work, we tackle three fundamental challenges in video-based planning: 1) unambiguous task communication with simple human instructions, 2) controllable video gen-eration that respects user intent, and 3) translating visual plans into robot actions. This& That adds gesture conditioning alongside language to generate video predictions as a suc-cinct and unambiguous alternative to existing language-only methods, especially in complex and uncertain environments. These video predictions are then fed into a behavior cloning architecture dubbed Diffusion Video to Action (DiVA), which outperforms prior state-of-the-art behavior cloning and video-based planning methods by substantial margins. Project web-site: https://this-and-that-vid.github.io/this-and-thatl.

Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these issues, we propose a scalable data generation pipeline that automatically augments expert demonstrations with failure recovery trajectories and fine-grained language annotations for training. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor frame-work, which combines failure recovery data with rich language descriptions to enhance robot control. RACER features a vision-language model (VLM) that acts as an online supervisor, providing detailed language guidance for error correction and task execution, and a language-conditioned visuomotor policy as an actor to predict the next actions. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer (RVT) on RLbench across various evaluation settings, including standard long-horizon tasks, dynamic goal-change tasks and zero-shot unseen tasks, achieving superior performance in both simulated and real world environments. Videos and code are available at: https://rich-language-failure-recovery.github.io.

Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. Project details can be found at https://www.mmintlab.com/research/cttp/.

Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.

Dexterous manipulation requires careful reasoning over extrinsic contacts. The prevalence of deforming tools in human environments, the use of deformable sensors, and the increasing number of soft robots yields a need for approaches that enable dexterous manipulation through contact reasoning where not all contacts are well characterized by classical rigid body contact models. Here, we consider the case of a deforming tool dexterously manipulating a rigid object. We propose a hybrid learning and first-principles approach to the modeling of simultaneous motion and force transfer of tools and objects. The learned module is responsible for jointly estimating the rigid object's motion and the deformable tool's imparted contact forces. We then propose a Contact Quadratic Program to recover forces between the environment and object subject to quasi-static equilibrium and Coulomb friction. The results is a system capable of modeling both intrinsic and extrinsic motions, contacts, and forces during dexterous deformable manipulation. We train our method in simulation and show that our method outperforms baselines under varying block geometries and physical properties, during pushing and pivoting manipulations, and demonstrate transfer to real world interactions. Video results can be found at https://deform-rigid-contact.github.io/.

One of the central challenges preventing robots from acquiring complex manipulation skills is the prohibitive cost of collecting large-scale robot demonstrations. In contrast, humans are able to learn efficiently by watching others interact with their environment. To bridge this gap, we introduce semantic action flow as a core intermediate representation capturing the essential spatio-temporal manipulator-object interactions, invariant to superficial visual differences. We present ViSA-Flow, a framework that learns this representation self-supervised from unlabeled large-scale video data. First, a generative model is pre-trained on semantic action flows automatically extracted from large-scale human-object interaction video data, learning a robust prior over manipulation structure. Second, this prior is efficiently adapted to a target robot by fine-tuning on a small set of robot demonstrations processed through the same semantic abstraction pipeline. We demonstrate through extensive experiments on the CALVIN benchmark and real-world tasks that ViSA-Flow achieves state-of-the-art performance, particularly in low-data regimes, outperforming prior methods by effectively transferring knowledge from human video observation to robotic execution. Videos are available at https://visaflow-web.github.io/ViSAFLOW.

Joint estimation of grasped object pose and extrinsic contacts is central to robust and dexterous manipulation. In this paper, we introduce MultiSCOPE, a state-estimation algorithm that leverages sequential frictional contacts (e.g., pokes) to jointly estimate contact locations and grasped object poses using exclusively proprioception and tactile feedback. Our method addresses the problem of reducing object pose uncertainty by using two complementary particle filters over a series of actions: one to estimate contact location (CPFGrasp) and another to estimate object poses (SCOPE). Our method addresses uncertainty in both robot proprioception and force-torque measurements, which is important for estimating in-hand object pose in the real world. We implement and evaluate our approach on simulated and real-world single-arm and dual-arm robotic systems. We demonstrate that by bringing two objects into contact several times, the robots can infer contact location and object poses simultaneously.

This paper presents Rummaging Using Mutual Information (RUMI), a method for online generation of robot action sequences to gather information about the pose of a known movable object in visually-occluded environments. Focusing on contact-rich rummaging, our approach leverages mutual information between the object pose distribution and robot trajectory for action planning. From an observed partial point cloud, RUMI deduces the compatible object pose distribution and approximates the mutual information of it with workspace occupancy in real time. Based on this, we develop an information gain cost function and a reachability cost function to keep the object within the robot's reach. These are integrated into a model predictive control (MPC) framework with a stochastic dynamics model, updating the pose distribution in a closed loop. Key contributions include a new belief framework for object pose estimation, an efficient information gain computation strategy, and a robust MPC-based control scheme. RUMI demonstrates superior performance in both simulated and real tasks compared to baseline methods.

Reconstructing unknown external source functions is an important perception capability for a large range of robotics domains including manipulation, aerial, and underwater robotics. In this work, we propose a Physics-Informed Neural Network (PINN [1]) based approach for solving the inverse source problems in robotics, jointly identifying unknown source functions and the complete state of a system given partial and noisy observations. Our approach demonstrates several advantages over prior works (Finite Element Methods (FEM) and data-driven approaches): it offers flexibility in integrating diverse constraints and boundary conditions; eliminates the need for complex discretizations (e.g., meshing); easily accommodates gradients from real measurements; and does not limit performance based on the diversity and quality of training data. We validate our method across three simulation and real-world scenarios involving up to 4th order partial differential equations (PDEs), constraints such as Signorini and Dirichlet, and various regression losses including Chamfer distance and L2 norm.

Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. Project details can be found at https://www.mmintlab.com/research/cttp/.

Tactile sensing provides robots with rich feedback during manipulation, enabling a host of perception and controls capabilities. Here, we present a new open-source, vision-based tactile sensor designed to promote reproducibility and accessibility across research and hobbyist communities. Building upon the GelSlim 3.0 sensor, our design features two key improvements: a simplified, modifiable finger structure and easily manufacturable lenses. To complement the hardware, we provide an open-source perception library that includes depth and shear field estimation algorithms to enable in-hand pose estimation, slip detection, and other manipulation tasks. Our sensor is accompanied by comprehensive manufacturing documentation, ensuring the design can be readily produced by users with varying levels of expertise. We validate the sensor's reproducibility through extensive human usability testing. For documentation, code, and data, please visit the project website: https://www.mmintlab.com/research/gelslim-4-0/

Developing robust and correctable visuomotor policies for robotic manipulation is challenging due to the lack of self-recovery mechanisms from failures and the limitations of simple language instructions in guiding robot actions. To address these issues, we propose a scalable data generation pipeline that automatically augments expert demonstrations with failure recovery trajectories and fine-grained language annotations for training. We then introduce Rich languAge-guided failure reCovERy (RACER), a supervisor-actor framework, which combines failure recovery data with rich language descriptions to enhance robot control. RACER features a vision-language model (VLM) that acts as an online supervisor, providing detailed language guidance for error correction and task execution, and a language-conditioned visuomotor policy as an actor to predict the next actions. Our experimental results show that RACER outperforms the state-of-the-art Robotic View Transformer (RVT) on RLbench across various evaluation settings, including standard long-horizon tasks, dynamic goal-change tasks and zero-shot unseen tasks, achieving superior performance in both simulated and real world environments. Videos and code are available at: https://rich-language-failure-recovery.github.io.

In-hand object manipulation is an important capability for dexterous manipulation. In this paper, we introduce a modeling and planning framework for in-hand object reconfiguration, focusing on frictional patch contacts between the robot's palms (or fingers) and the object. Our approach leverages two cooperative patch contacts on either side of the object to iteratively reposition it within the robot's grasp by alternating between sliding and sticking motions. Unlike previous methods that rely on single-point contacts or restrictive assumptions on contact dynamics, our framework models the complex interaction of dual frictional patches, allowing for greater control over object motion. We develop a planning algorithm that computes feasible motions to reorient and re-grasp objects without causing unintended slippage. We demonstrate the effectiveness of our approach in simulation and real-world experiments, showing significant improvements in object stability and pose accuracy across various object geometries.

Tactile sensing is a widely-studied means of implicit communication between robot and human. In this paper, we investigate how tactile sensing can help bridge differences between robotic embodiments in the context of collaborative manipulation. For a robot, learning and executing force-rich collaboration require compliance to human interaction. While compliance is often achieved with admittance control, many commercial robots lack the joint torque monitoring needed for such control. To address this challenge, we present an approach that uses tactile sensors and behavior cloning to transfer policies from robots with these capabilities to those without. We train a single policy that demonstrates positive transfer across embodiments, including robots without torque sensing. We demonstrate this positive transfer on four different tactile-enabled embodiments using the same policy trained on force-controlled robot data. Across multiple proposed metrics, the best performance came from a decomposed tactile shear-field representation combined with a pre-trained encoder, which improved success rates over alternative representations.

Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.

Tactile sensing has proven to be an invaluable tool for enhancing robotic perception, particularly in scenarios where visual data is limited or unavailable. However, traditional methods for pose estimation using tactile data often rely on intricate modeling of sensor mechanics or estimation of contact patches, which can be cumbersome and inherently deterministic. In this work, we introduce Tactile Neural De-rendering, a novel approach that leverages a generative model to reconstruct a local 3D representation of an object based solely on its tactile signature. By rendering the object as though perceived by a virtual camera embedded at the fingertip, our method provides a more intuitive and flexible representation of the tactile data. This 3D reconstruction not only facilitates precise pose estimation but also allows for the quantification of uncertainty, providing a robust framework for tactile-based perception in robotics.

Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.

This paper presents Rummaging Using Mutual Information (RUMI), a method for online generation of robot action sequences to gather information about the pose of a known movable object in visually-occluded environments. Focusing on contact-rich rummaging, our approach leverages mutual information between the object pose distribution and robot trajectory for action planning. From an observed partial point cloud, RUMI deduces the compatible object pose distribution and approximates the mutual information of it with workspace occupancy in real time. Based on this, we develop an information gain cost function and a reachability cost function to keep the object within the robot's reach. These are integrated into a model predictive control (MPC) framework with a stochastic dynamics model, updating the pose distribution in a closed loop. Key contributions include a new belief framework for object pose estimation, an efficient information gain computation strategy, and a robust MPC-based control scheme. RUMI demonstrates superior performance in both simulated and real tasks compared to baseline methods.

Clear, interpretable instructions are invaluable when attempting any complex task. Good instructions help to clarify the task and even anticipate the steps needed to solve it. In this work, we propose a robot learning framework for communicating, planning, and executing a wide range of tasks, dubbed This&amp;That. This&amp;That solves general tasks by leveraging video generative models, which, through training on internet-scale data, contain rich physical and semantic context. In this work, we tackle three fundamental challenges in video-based planning: 1) unambiguous task communication with simple human instructions, 2) controllable video generation that respects user intent, and 3) translating visual plans into robot actions. This&amp;That uses language-gesture conditioning to generate video predictions, as a succinct and unambiguous alternative to existing language-only methods, especially in complex and uncertain environments. These video predictions are then fed into a behavior cloning architecture dubbed Diffusion Video to Action (DiVA), which outperforms prior state-of-the-art behavior cloning and video-based planning methods by substantial margins.

In this paper, we consider the problem of non-prehensile manipulation using grasped objects. This problem is a superset of many common manipulation skills including instances of tool-use (e.g., grasped spatula flipping a burger) and assembly (e.g., screwdriver tightening a screw). Here, we present an algorithmic approach for non-prehensile manipulation leveraging a gripper with highly compliant and high-resolution tactile sensors. Our approach solves for robot actions that drive object poses and forces to desired values while obeying the complex dynamics induced by the sensors as well as the constraints imposed by static equilibrium, object kinematics, and frictional contact. Our method is able to produce a variety of manipulation skills and is amenable to gradient-based optimization by exploiting differentiability within contact modes (e.g., specifications of sticking or sliding contacts). We evaluate 4 variants of controllers that attempt to realize these plans and demonstrate a number of complex skills including non-prehensile planar sliding and pivoting on a variety of object geometries. The perception and controls capabilities that drive these skills are the building blocks towards dexterous and reactive autonomy in unstructured environments.

Robotic high-viscosity fluid deposition plays a pivotal role in various manufacturing applications including adhesive and sealant dispensing, as well as in the additive manufacturing of deformable materials, such as those employed in soft robotics. Uncompensated high-viscosity fluid deposition can lead to poor part quality and defects due to large transient delays and complex fluid dynamics. In this letter, we propose a lumped-parameter flow model and compensation strategies to address significant transient delays and nonlinearity inherent in high-viscosity fluid deposition using a robotic manipulator. Our computationally efficient model is well-suited to real-time control and can be calibrated in minutes. Our compensation strategies leverage an iterative Linear-Quadratic Regulator to compute compensated deposition paths that can be deployed on robotic dispensing systems. These paths can either be deployed offline or corrected live via feedback from our proposed vision-based flow sensor. To validate the effectiveness of our approach, we conducted experiments extruding high-viscosity liquid silicone using a Kuka lbr iiwa robot. Comparative analysis with several baseline protocols demonstrates that our proposed method significantly improves material deposition within desired boundaries.

This paper proposes a novel method for estimating the set of plausible poses of a rigid object from a set of points with volumetric information, such as whether each point is in free space or on the surface of the object.In particular, we study how pose can be estimated from force and tactile data arising from contact.Using data derived from contact is challenging because it is inherently less information-dense than visual data, and thus the pose estimation problem is severely under-constrained when there are few contacts.Rather than attempting to estimate the true pose of the object, which is not tractable without a large number of contacts, we seek to estimate a plausible set of poses which obey the constraints imposed by the sensor data.Existing methods struggle to estimate this set because they are either designed for single pose estimates or require informative priors to be effective.Our approach to this problem, Constrained pose Hypothesis Set Elimination (CHSEL), has three key attributes: 1) It considers volumetric information, which allows us to account for known free space; 2) It uses a novel differentiable volumetric cost function to take advantage of powerful gradient-based optimization tools; and 3) It uses methods from the Quality Diversity (QD) optimization literature to produce a diverse set of high-quality poses.To our knowledge, QD methods have not been used previously for pose registration.We also show how to update our plausible pose estimates online as more data is gathered by the robot.Our experiments suggest that CHSEL shows large performance improvements over several baseline methods for both simulated and real-world data. 1

In this paper, we propose a method for estimating in-hand object poses using proprioception and tactile feedback from a bimanual robotic system.Our method addresses the problem of reducing pose uncertainty through a sequence of frictional contact interactions between the grasped objects.As part of our method, we propose 1) a tool segmentation routine that facilitates contact location and object pose estimation, 2) a loss that allows reasoning over solution consistency between interactions, and 3) a loss to promote converging to object poses and contact locations that explain the external forcetorque experienced by each arm.We demonstrate the efficacy of our method in a task-based demonstration both in simulation and on a real-world bimanual platform and show significant improvement in object pose estimation over single interactions.Visit www.mmintlab.

Reasoning over the interplay between object deformation and force transmission through contact is central to the manipulation of compliant objects.In this paper, we propose Neural Deforming Contact Field (NDCF), a representation that jointly models object deformations and contact patches from visuo-tactile feedback using implicit representations.Representing the object geometry and contact with the environment implicitly allows a single model to predict contact patches of varying complexity.Additionally, learning geometry and contact simultaneously allows us to enforce physical priors, such as ensuring contacts lie on the surface of the object.We propose a neural network architecture to learn a NDCF, and train it using simulated data.We then demonstrate that the learned NDCF transfers directly to the real-world without the need for finetuning.We benchmark our proposed approach against a baseline representing geometry and contact patches with point clouds.We find that NDCF performs better on simulated data and in transfer to the real-world.More details and video results can be found at https://www.mmintlab.com/ndcf/.

In this paper, we discuss the mechanics and planning algorithms to slide an object on a horizontal planar surface via frictional patch contact made with its top surface.Here, we propose an asymmetric dual limit surface model to determine slip boundary conditions for both the top and bottom contact.With this model, we obtain a range of twists that can keep the object in sticking contact with the robot end-effector while slipping on the supporting plane.Based on these constraints, we derive a planning algorithm to slide objects with only top contact to arbitrary goal poses without slippage between end effector and the object.We validate the proposed model empirically and demonstrate its predictive accuracy on a variety of object geometries and motions.We also evaluate the planning algorithm over a variety of objects and goals demonstrate an orientation error improvement of 90% when compared to methods naive to linear path planners.For more results and information, please visit https://www.mmintlab.com/dual-limit-surfaces/.