Quan Nguyen

Bipedal locomotion control is essential for humanoid robots to navigate complex, human-centric environments. While optimization-based control designs are popular for integrating sophisticated models of humanoid robots, they often require labor-intensive manual tuning. In this work, we address the challenges of parameter selection in bipedal locomotion control using DiffTune, a model-based autotuning method that leverages differential programming for efficient parameter learning. A major difficulty lies in balancing model fidelity with differentiability. We address this difficulty using a low-fidelity model for differentiability, enhanced by a Ground Reaction Force-and-Moment Network (GRFM-Net) to capture discrepancies between MPC commands and actual control effects. We validate the parameters learned by DiffTune with GRFM-Net in hardware experiments, which demonstrates the parameters’ optimality in a multi-objective setting compared with baseline parameters, reducing the total loss by up to 40.5% compared with the expert-tuned parameters. The results confirm the GRFM-Net’s effectiveness in mitigating the sim-to-real gap, improving the transferability of simulation-learned parameters to real hardware.

In recent years, several studies—conducted by both industry and U.S. Department of Energy (DOE)-funded initiatives—have proposed linking North America's Eastern and Western Interconnections (EI and WI) through a multiterminal DC (MTDC) macrogrid. These studies have explored the advantages and opportunities of the proposed configuration from the perspectives of capacity sharing and frequency support. However, the potential challenges of small-signal stability arising from this interconnection have not been thoroughly examined. To address this gap, detailed model-based simulation studies are performed in this paper to assess the risks of poorly damped inter-area oscillations in the proposed macrogrid. A custom-built dynamic model of the MTDC system is developed and integrated with industry-grade models of the EI and WI, incorporating high levels of inverter-based energy resources. Through model-based oscillation analysis, potential shifts in inter-area modes—for both EI and WI, resulting from the MTDC integration are characterized, and modes with inadequate damping are identified. Furthermore, to mitigate the risks of unstable oscillations, supplementary damping controllers are designed for the MTDC system, leveraging wide-area feedback to modulate active power set points at selected converter stations. A frequency scanning approach is employed for data-driven model linearization and controller synthesis. The damping performance is evaluated under the designed operating conditions and selected contingency scenarios.

Legged robots can significantly increase human productivity by performing delivery tasks. Legged robots cannot be tethered to power sources when operating in large outdoor environments. If a robot runs out of energy while executing a task, it will require human intervention, resulting in delays. On the other hand, frequent battery recharging or replacement could also lead to significant delays in task completion. This paper presents an energy-aware hierarchical planning approach that accounts for energy consumption and integrates appropriate battery replacement strategies to ensure that tasks are completed efficiently. Our algorithm generates graph search instances for varying battery replacement actions, the option to split the payload into smaller portions, and reducing speed in the first level, while running graph search to determine the optimal plan that minimizes the time to complete the delivery task. We illustrate the effectiveness of our planning approach on a terrain with varying slopes and delivery tasks with different requirements.

Collaborative loco-manipulation by multiple quadruped manipulators enables handling bulky, heavy objects beyond the capabilities of individual robots. However, coordinating robot teams while navigating complex terrains and avoiding obstacles remains challenging. We propose a hierarchical control framework consisting of a model predictive control (MPC)-based manipulation planner with integrated obstacle avoidance, a geometry-aware mapping converting object trajectories into robot commands, and decentralized loco-manipulation MPC controllers. The framework supports collision-free collaborative manipulation tasks and enhances payload capacities. Validation through simulation and real-world hardware experiments with diverse quadruped robot teams demonstrates the approach’s effectiveness, robustness, and practical applicability.

Current humanoid push-recovery strategies often use whole-body motion, yet they tend to overlook posture regulation. For instance, in manipulation tasks, the upper body may need to stay upright and have minimal recovery displacement. This paper introduces a novel approach to enhancing humanoid push-recovery performance under unknown disturbances and regulating body posture by tailoring the recovery stepping strategy. We propose a hierarchical-MPC-based scheme that analyzes and detects instability in the prediction window and quickly recovers through adapting gait frequency. Our approach integrates a high-level nonlinear MPC, a posture-aware gait frequency adaptation planner, and a low-level convex locomotion MPC. The planners predict the center of mass (CoM) state trajectories that can be assessed for precursors of potential instability and posture deviation. In simulation, we demonstrate improved maximum recoverable impulse by 131 % on average compared with baseline approaches. In hardware experiments, a 125 ms advancement in recovery stepping timing/reflex has been observed with the proposed approach. We also demonstrate improved push-recovery performance and minimized body attitude change under 0.2 rad.

Performing acrobatic maneuvers involving long aerial phases, such as precise dives or multiple backflips from significant heights, remains an open challenge in legged robot autonomy. Such aggressive motions often require accurate state predictions over long horizons with multiple contacts and extended flight phases. Most existing trajectory optimization (TO) methods rely on Euler or Runge-Kutta integration, which can accumulate significant prediction errors over long planning horizons. In this work, we propose a novel whole-body TO method using variational integration (VI) and full-body nonlinear dynamics for long-flight aggressive maneuvers. Compared to traditional Euler-based TO, our approach using VI preserves energy and momentum properties of the continuous-time system and reduces error between predicted and executed trajectories by factors of between 2 - 10 while achieving similar planning time. We successfully demonstrate long-flight triple backflips on a quadruped A1 robot model and backflips on a bipedal HECTOR robot model for various heights and distances, achieving landing angle errors of only a few degrees. In contrast, TO with Euler integration fails to achieve accurate landings in equivalent circumstances, e.g., with landing angle errors greater than 90° for triple backflips. We provide an open-source implementation of our VI -discretized TO to support further research on accurate dynamic maneuvers for multi-rigid-body robot systems with contact: https://github.com/DRCL-USC/VI_discretized_TO

The scarcity of high-quality multimodal biomedical data limits the ability to effectively fine-tune pretrained Large Language Models (LLMs) for specialized biomedical tasks. To address this challenge, we introduce MINT (Multimodal Integrated kNowledge Transfer), a framework that aligns unimodal large decoder models with domain-specific decision patterns from high-quality multimodal biomedical data through preference optimization. While MINT supports different optimization techniques, we primarily implement it with the Odds Ratio Preference Optimization (ORPO) framework as its backbone. This strategy enables the aligned LLMs to perform predictive tasks using text-only or image-only inputs while retaining knowledge learnt from multimodal data. MINT leverages an upstream multimodal machine learning (MML) model trained on high-quality multimodal data to transfer domain-specific insights to downstream text-only or image-only LLMs. We demonstrate MINT's effectiveness through two key applications: (1) Rare genetic disease prediction from texts, where MINT uses a multimodal encoder model, trained on facial photos and clinical notes, to generate a preference dataset for aligning a lightweight decoder-based text-only LLM (Llama 3.2-3B-Instruct). Despite relying on text input only, the MINT-derived model outperforms models trained with Supervised Fine-Tuning (SFT), Retrieval-Augmented Generation (RAG), or direct preference optimization (DPO), and even outperforms much larger foundation model (Llama 3.1-405B-Instruct). (2) Tissue type classification using cell nucleus images, where MINT uses a vision-language foundation model as the preference generator, containing knowledge learnt from both text and histopathological images to align downstream image-only models. The resulting MINT-derived model significantly improves the performance of Llama 3.2-Vision-11B-Instruct on tissue type classification. In summary, MINT provides an effective strategy to align unimodal LLMs with high-quality multimodal expertise through preference optimization. Our study also highlights a hybrid strategy that grafts the strength of encoder models in classification tasks into large decoder models to enhance reasoning, improve predictive tasks and reduce hallucination in biomedical applications.

Mammography plays a pivotal role in breast cancer diagnosis and monitoring, yet the accuracy of Breast Imaging-Reporting and Data System (BI-RADS) assessments can vary among radiologists, particularly concerning breast density evaluations. Computer-aided diagnosis (CADx) systems have emerged to augment diagnostic precision. In this context, we propose a redesigned Dual-Task Learning (DTL) framework for mammography screening, focusing on BI-RADS and breast density classification. Our approach, notably DTL-Variant M, demonstrates superior performance across multiple metrics. DTL-Variant M showcases substantial enhancements in both BI-RADS and breast density classification tasks compared to other variants, empha-sizing its efficiency with ResNeXt-50 backbone. Furthermore, we employ focal loss, a highly effective loss function for imbalanced data, in our approach to tackle the problem of class imbalance and achieve better results for BI-RADS classification, which we consider more significant than density classification when using cross-entropy loss.

Accurate obstetric ultrasound segmentation is hampered by speckle noise and scarce annotations. We propose OASIS-Net, a dual-space adversarial semi-supervised framework that trains a single DeepLabV3$+$ backbone by minimizing one unified consistency loss. The loss couples input-space adversaries (iterative FGSM with $K=3$ steps, $\epsilon =4/255$) and weight-space gradient-aligned perturbations (DGAP, weight scale $=0.5$) whose influence grows with a sigmoid ramp ($T_{\text{ramp}}=20$, $\alpha _{\max }=1.0$). Pseudo-labels are accepted with a confidence threshold of 0.95 and the unlabeled loss weight is 1.0. We evaluate OASIS-Net on two public obstetric benchmarks: FUGC (50 labeled, 450 unlabeled) and PSFH (5,101 frames, 70% unlabeled). Using 20% of labels, the method attains Dice = 96.53% and HD$_{95}$ = 3.86 px on FUGC, and Dice = 97.16% and HD$_{95}$ = 2.34 px on PSFH. Ablation shows that removing either perturbation stream reduces Dice by up to 1.8 percentage points. The trained model runs at 18.96 frames s$^{-1}$ on a single RTX 4060 Ti and produces high-precision masks that enable automated cervical-length and angle-of-progression measurements for objective obstetric screening and intrapartum monitoring. These results demonstrate that jointly enforcing input- and parameter-space adversarial consistency yields a label-efficient, robust solution for obstetric ultrasound segmentation and supports real-time clinical use.

Achieving both target accuracy and robustness in dynamic maneuvers with long flight phases, such as high or long jumps, has been a significant challenge for legged robots. To address this challenge, we propose a novel learning-based control approach consisting of model learning and model predictive control (MPC) utilizing a variable-frequency scheme. Compared to existing MPC techniques, we learn a model directly from experiments, accounting not only for leg dynamics but also for modeling errors and unknown dynamics mismatch in hardware and during contact. Additionally, learning the model with variable-frequency allows us to cover the entire flight phase and final jumping target, enhancing the prediction accuracy of the jumping trajectory. Using the learned model, we also design variable-frequency to effectively leverage different jumping phases and track the target accurately. In a total of 92 jumps on Unitree A1 robot hardware, we verify that our approach outperforms other MPCs using fixed-frequency or nominal model, reducing the jumping distance error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$2-8$</tex-math></inline-formula> times. We also achieve jumping distance errors of less than 3% during continuous jumping on uneven terrain with randomly-placed perturbations of random heights (up to 4 cm or 27% the robot's standing height). Our approach obtains distance errors of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1-2$</tex-math></inline-formula> cm on 34 single and continuous jumps with different jumping targets and model uncertainties.

Bipedal locomotion control is essential for humanoid robots to navigate complex, human-centric environments. While optimization-based control designs are popular for integrating sophisticated models of humanoid robots, they often require labor-intensive manual tuning. In this work, we address the challenges of parameter selection in bipedal locomotion control using DiffTune, a model-based autotuning method that leverages differential programming for efficient parameter learning. A major difficulty lies in balancing model fidelity with differentiability. We address this difficulty using a low-fidelity model for differentiability, enhanced by a Ground Reaction Force-and-Moment Network (GRFM-Net) to capture discrepancies between MPC commands and actual control effects. We validate the parameters learned by DiffTune with GRFM-Net in hardware experiments, which demonstrates the parameters' optimality in a multi-objective setting compared with baseline parameters, reducing the total loss by up to 40.5$\%$ compared with the expert-tuned parameters. The results confirm the GRFM-Net's effectiveness in mitigating the sim-to-real gap, improving the transferability of simulation-learned parameters to real hardware.

Abstract Implementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms.

In this paper, we consider a general task of jumping varying distances and heights for a quadrupedal robot in noisy environments , such as off of uneven terrain and with variable robot dynamics parameters. To accurately jump in such conditions, we propose a framework using deep reinforcement learning that leverages and augments the complex solution of nonlinear trajectory optimization for quadrupedal jumping. While the standalone optimization limits jumping to take-off from flat ground and requires accurate assumptions of robot dynamics, our proposed approach improves the robustness to allow jumping off of significantly uneven terrain with variable robot dynamical parameters and environmental conditions. Compared with walking and running, the realization of aggressive jumping on hardware necessitates accounting for the motors’ torque-speed relationship as well as the robot’s total power limits. By incorporating these constraints into our learning framework, we successfully deploy our policy sim-to-real without further tuning, fully exploiting the available onboard power supply and motors. We demonstrate robustness to environment noise of foot disturbances of up to 6 cm in height, or 33% of the robot’s nominal standing height, while jumping 2 x the body length in distance.

In agricultural robotics, the development of practical and scalable autonomy solutions is crucial for operational efficiency and effectiveness. Autonomous navigation, the cornerstone capability for robots, is the basis for any agricultural tasks such as crop monitoring, weed management, and crop transportation. This study centers on developing foundation navigation autonomy for quadruped robots in agricultural settings with experimental validations in strawberry field scenarios. Our approach removes the reliance on costly sensors, opting instead for a vision-based system that ensures robustness and reliability in navigating strawberry fields. Through field testing, we demonstrate that our navigation autonomy achieves precise furrow entry, tracking, and exit. The robot exhibits exceptional mobility, adeptly handling muddy and uneven terrain with the aid of vision. Furthermore, our foundational autonomy framework effectively detects obstacle dimensions for both planning and control to ensure safe interactions between robots and human workers in the field. This work not only presents a leap in agricultural robotics autonomy but also lays the groundwork for broader applications of quadruped robots in complex, real-world scenarios.

Delays play an important role in the overall performance of multi-robot missions. Delays can be handled in a reactive manner by replanning or can be handled proactively by selecting task allocations that are robust to delays. Mixed Integer Linear Programming (MILP) has emerged as a useful tool for solving multi-robot task allocation problems. The possibility of delays can be incorporated by sampling many different delay scenarios and generating an optional solution for each scenario. The solution that leads to the most robust performance is selected as the overall solution. Unfortunately, generating a large number of delay scenarios and computing the robustness of each solution by evaluating it using every sampled delay scenario is computationally very slow. This paper presents a method for speeding up computations by pruning solutions based on a similarity index. We evaluate the proposed approach using assembly and disinfection applications. We show that our approach leads to very good solutions with a modest computational effort.

Abstract In this work, we present a step toward boosting human picker efficiency in strawberry harvesting using quadruped robots. Transitioning from manual to automated harvesting in the fruit industry, especially for fresh-market strawberries, remains a challenge due to the delicate nature of the fruit and unique field layouts. A notable inefficiency in manual harvesting is the time human pickers spend transporting filled trays to collection stations. Quadruped robots, renowned for their adaptability in challenging terrains, emerge as promising candidates for mobile crop transportation. Extending the foundational concepts from previous work, we introduce quadruped robots as auxiliary support units specifically tailored to boost human picker efficiency. Such legged platforms have demonstrated their mobility in agricultural scenarios. The robots can traverse various agricultural terrains, including rugged and muddy furrows in the field and main irrigation lines at the headland. Beyond advancements in transportation robot hardware, our system introduces a novel predictive scheduling algorithm, aiming to enhance harvesting efficiency. Specifically, this algorithm is engineered for real-time replanning, to account for unpredictable picker behaviors. Our empirical results show that the integration of quadruped robots leads to 15% decrease in total makespan of the task and a 72% decrease in non-picking time, compared to traditional manual methods.

Hybrid dynamical systems with nonlinear dynamics are one of the most general modeling tools for representing robotic systems, especially contact-rich systems. However, providing guarantees regarding the safety or performance of nonlinear hybrid systems remains a challenging problem because it requires simultaneous reasoning about continuous state evolution and discrete mode switching. In this work, we address this problem by extending classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear dynamical systems, to hybrid dynamical systems. We characterize the reachable sets for hybrid systems through a generalized value function defined over discrete and continuous states of the hybrid system. We also provide a numerical algorithm to compute this value function and obtain the reachable set. Our framework can compute reachable sets for hybrid systems consisting of multiple discrete modes, each with its own set of nonlinear continuous dynamics, discrete transitions that can be directly commanded or forced by a discrete control input, while still accounting for control bounds and adversarial disturbances in the state evolution. Along with the reachable set, the proposed framework also provides an optimal continuous and discrete controller to ensure system safety. We demonstrate our framework in several simulation case studies, as well as on a real-world testbed to solve the optimal mode planning problem for a quadruped with multiple gaits.

This report describes a generic virtual synchronous machine (VSM) grid-forming inverter (GFM) model - REGFM_B1. The initial model specification was proposed by Pacific Northwest National Laboratory (PNNL), General Electric (GE), and Electric Power Research Institute (EPRI). Siemens Gamesa Renewable Energy (SGRE) also provided inputs to the specification. The model specification has been revised multiple times based on the discussions between all the contributors listed in this report. This work was funded by the Universal Interoperability for Grid-Forming Inverters (UNIFI) Consortium. This generic model is developed to help the utility industry understand the concept of VSM GFMs. The model could be used to represent equipment for long-term planning studies where vendor-specific models are not available. As equipment mature and improve, generic models will be updated to capture the new functionalities of GFMs. It is not intended that these models will always remain representative of all future GFM technologies.

In recent years, the field of legged robotics has seen growing interest in enhancing the capabilities of these robots through the integration of articulated robotic arms. However, achieving successful loco-manipulation, especially involving interaction with heavy objects, is far from straightforward, as object manipulation can introduce substantial disturbances that impact the robot’s locomotion. This paper presents a novel framework for legged loco-manipulation that considers whole-body coordination through a hierarchical optimization-based control framework. First, an online manipulation planner computes the manipulation forces and manipulated object task-based reference trajectory. Then, pose optimization aligns the robot’s trajectory with kinematic constraints. The resultant robot reference trajectory is executed via a linear MPC controller incorporating the desired manipulation forces into its prediction model. Our approach has been validated in simulation and hardware experiments, highlighting the necessity of whole-body optimization compared to the baseline locomotion MPC when interacting with heavy objects. Experimental results with Unitree Aliengo, equipped with a custom-made robotic arm, showcase its ability to lift and carry an 8kg payload and manipulate doors.

Many applications require the deployment of legged-robot teams to effectively and efficiently carry out missions. The use of multiple robots allows tasks to be executed concurrently, expediting mission completion. It also enhances resilience by enabling task transfer in case of a robot failure. This paper presents a formulation based on Mixed Integer Linear Programming (MILP) for allocating tasks to robots by taking into account travel time and ensuring efficient execution of collaborative tasks. We extended the MILP formulation to account for complexities with legged robot teams. Our results demonstrate that this approach leads to improved performance in terms of the makespan of the mission. We demonstrate the usefulness of this approach using a case study involving the disinfection of a building consisting of multiple rooms.

Dynamic and continuous jumping remains an open yet challenging problem in bipedal robot control. Real-time planning with full body dynamics over the entire jumping trajectory presents unsolved challenges in computation burden. In this paper, we propose a novel variable-model optimization approach, a unified framework of variable-model trajectory optimization (TO) and variable-frequency Model Predictive Control (MPC), to effectively realize continuous and robust jumping planning and control on HECTOR bipedal robot in real-time. The proposed TO fuses variable-fidelity dynamics modeling of bipedal jumping motion in different jumping phases to balance trajectory accuracy and real-time computation efficiency. In addition, conventional fixed-frequency control approaches suffer from unsynchronized sampling frequencies, leading to mismatched modeling resolutions. We address this by aligning the MPC sampling frequency with the variable-model TO trajectory resolutions across different phases. In hardware experiments, we have demonstrated robust and dynamic jumps covering a distance of up to 40 cm (57% of robot height). To verify the repeatability of this experiment, we run 53 jumping experiments and achieve 90% success rate. In continuous jumps, we demonstrate continuous bipedal jumping with terrain height perturbations (up to 5 cm) and discontinuities (up to 20 cm gap).

The efficacy of reinforcement learning for robot control relies on the tailored integration of task-specific priors and heuristics for effective exploration, which challenges their straightforward application to complex tasks and necessitates a unified approach. In this work, we define a general class for priors called oracles that generate state references when queried in a closed-loop manner during training. By bounding the permissible state around the oracle's ansatz, we propose a task-agnostic oracle-guided policy optimization. To enhance modularity, we introduce task-vital modes, showing that a policy mastering a compact set of modes and transitions can handle infinite-horizon tasks. For instance, to perform parkour on an infinitely long track, the policy must learn to jump, leap, pace, and transition between these modes effectively. We validate this approach in challenging bipedal control tasks: parkour and diving using a 16 DoF dynamic bipedal robot, HECTOR. Our method results in a single policy per task, solving parkour across diverse tracks and omnidirectional diving from varied heights up to 2m in simulation, showcasing versatile agility. We demonstrate successful sim-to-real transfer of parkour, including leaping over gaps up to 105 % of the leg length, jumping over blocks up to 20 % of the robot's nominal height, and pacing at speeds of up to 0.6 m/s, along with effective transitions between these modes in the real robot.

Agile-legged robots have proven to be highly effective in navigating and performing tasks in complex and challenging environments, including disaster zones and industrial settings. However, these applications commonly require the capability of carrying heavy loads while maintaining dynamic motion. Therefore, this paper presents a novel methodology for incorporating adaptive control into a force-based control system. Recent advancements in the control of quadruped robots show that force control can effectively realize dynamic locomotion over rough terrain. By integrating adaptive control into the force-based controller, our proposed approach can maintain the advantages of the baseline framework while adapting to significant model uncertainties and unknown terrain impact models. Experimental validation was successfully conducted on the Unitree A1 robot. With our approach, the robot can carry heavy loads (up to 50% of its weight) while performing dynamic gaits such as fast trotting and bounding across uneven terrains.

Autonomous collision avoidance requires accurate environmental perception; however, flight systems often possess limited sensing capabilities with field-of-view (FOV) restrictions. To navigate this challenge, we present a safety-aware approach for online determination of the optimal sensor-pointing direction <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\psi _\text{d}$</tex-math></inline-formula> which utilizes control barrier functions (CBFs). First, we generate a spatial density function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> which leverages CBF constraints to map the collision risk of all local coordinates. Then, we convolve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Phi$</tex-math></inline-formula> with an attitude-dependent sensor FOV quality function to produce the objective function <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> which quantifies the total observed risk for a given pointing direction. Finally, by finding the global optimizer for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\Gamma$</tex-math></inline-formula> , we identify the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\psi _\text{d}$</tex-math></inline-formula> which maximizes the perception of risk within the FOV. We incorporate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\psi _\text{d}$</tex-math></inline-formula> into a safety-critical flight architecture and conduct a numerical analysis using multiple simulated mission profiles. Our algorithm achieves a success rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{88}-\text{96}\%$</tex-math></inline-formula> , constituting a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{16}-\text{29}\%$</tex-math></inline-formula> improvement compared to the best heuristic methods. We demonstrate the functionality of our approach via a flight demonstration using the Crazyflie 2.1 micro-quadrotor. Without a priori obstacle knowledge, the quadrotor follows a dynamic flight path while simultaneously calculating and tracking <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\psi _\text{d}$</tex-math></inline-formula> to perceive and avoid two static obstacles with an average computation time of 371 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> s.

The agility of animals, particularly in complex activities such as running, turning, jumping, and backflipping, stands as an exemplar for robotic system design. Transferring this suite of behaviors to legged robotic systems introduces essential inquiries: How can a robot learn multiple locomotion behaviors simultaneously? How can the robot execute these tasks with a smooth transition? How to integrate these skills for wide-range applications? This paper introduces the Versatile Instructable Motion prior (VIM) - a Reinforcement Learning framework designed to incorporate a range of agile locomotion tasks suitable for advanced robotic applications. Our framework enables legged robots to learn diverse agile low-level skills by imitating animal motions and manually designed motions. Our Functionality reward guides the robot's ability to adopt varied skills, and our Stylization reward ensures that robot motions align with reference motions. Our evaluations of the VIM framework span both simulation and the real world. Our framework allows a robot to concurrently learn diverse agile locomotion skills using a single learning-based controller in the real world. Videos can be found on our website: https://rchalyang.github.io/VIM/

Hybrid dynamical systems with nonlinear dynamics are one of the most general modeling tools for representing robotic systems, especially contact-rich systems. However, providing guarantees regarding the safety or performance of nonlinear hybrid systems remains a challenging problem because it requires simultaneous reasoning about continuous state evolution and discrete mode switching. In this work, we address this problem by extending classical Hamilton-Jacobi (HJ) reachability analysis, a formal verification method for continuous-time nonlinear dynamical systems, to hybrid dynamical systems. We characterize the reachable sets for hybrid systems through a generalized value function defined over discrete and continuous states of the hybrid system. We also provide a numerical algorithm to compute this value function and obtain the reachable set. Our framework can compute reachable sets for hybrid systems consisting of multiple discrete modes, each with its own set of nonlinear continuous dynamics, discrete transitions that can be directly commanded or forced by a discrete control input, while still accounting for control bounds and adversarial disturbances in the state evolution. Along with the reachable set, the proposed framework also provides an optimal continuous and discrete controller to ensure system safety. We demonstrate our framework in several simulation case studies, as well as on a real-world testbed to solve the optimal mode planning problem for a quadruped with multiple gaits.

This paper presents an analog Kalman filter employing a shared VCO scheme that takes sensor data from a gyroscope and an accelerometer as input and generates a digitized estimate of the attitude via sensor fusion. The VCO-based integrator and ADC are designed to consume minimal power and implementation overhead by sharing a CMOS thyristor-based VCO operating in the subthreshold region. The silicon prototype is fabricated in 65 nm technology and achieves 0.8° RMSE using real sensor data, and the embedded ADC measures 51.58 dB SNDR and 57.28 dB SFDR under a single-tone test.

This paper presents a novel method to control humanoid robot dynamic loco-manipulation with multiple contact modes via multi-contact Model Predictive Control (MPC) framework. The proposed framework includes a multi-contact dynamics model capable of capturing various contact modes in loco-manipulation, such as hand-object contact and foot-ground contacts. Our proposed dynamics model represents the object dynamics as an external force acting on the system, which simplifies the model and makes it feasible for solving the MPC problem. In numerical validations, our multi-contact MPC framework only needs contact timings of each task and desired states to give MPC the knowledge of changes in contact modes in the prediction horizons in loco-manipulation. The proposed framework can control the humanoid robot to complete multitasks dynamic loco-manipulation applications such as efficiently picking up and dropping off objects while turning and walking.

Recent studies on quadruped robots have focused on either locomotion or mobile manipulation using a robotic arm. However, legged robots can manipulate large objects using non-prehensile manipulation primitives, such as planar pushing, to drive the object to the desired location. This paper presents a novel hierarchical model predictive control (MPC) for contact optimization of the manipulation task. Using two cascading MPCs, we split the loco-manipulation problem into two parts: the first to optimize both contact force and contact location between the robot and the object, and the second to regulate the desired interaction force through the robot locomotion. Our method is successfully validated in both simulation and hardware experiments. While the baseline locomotion MPC fails to follow the desired trajectory of the object, our proposed approach can effectively control both object's position and orientation with minimal tracking error. This capability also allows us to perform obstacle avoidance for both the robot and the object during the loco-manipulation task.

We present a new framework for implementing real-time embedded safety-critical controllers which utilizes hybrid computing to address the issue of limited computational resources, a problem that is particularly prevalent in microrobotics. In our approach, the nominal stabilizing control algorithm is implemented digitally while the safety-critical quadratic program is solved via a dedicated analog resistor array. We apply this hybrid computing architecture to a simulated collision avoidance task for a micro-aerial vehicle and show the benefit relative to a purely-digital implementation. By leveraging analog quadratic programming on the Crazyflie 2.1 micro quadrotor, a reduction in overall processing time from 8.9 ms to 0.6 ms is estimated for this computationally-limited system. We further display the viability of our proposed safety-critical control framework through real-time flight demonstrations, utilizing a novel prototype analog circuit tethered to the Crazyflie. The flight results confirm the functionality of the control structure and prototype circuit while highlighting the overall capabilities of hybrid computing.

Quadratic programming (QP) problems are common in many applications requiring optimization, including automatic control systems, finance analysis, chemical processes, etc. Typical QP problems are solved digitally via an iterative algorithm with userdefined error tolerance for the final solution $[1,2]$. Consequently, the solution accuracy of QP solvers naturally tradeoffs with the latency, i.e., the time required to derive an acceptable solution. When both high accuracy and low latency are demanded, conventional digital QP solvers can be infeasible or impose a significant overhead. Alternatively, analog QP solvers [3], [4] have been shown to potentially reduce the latency while maintaining the same accuracy. However, the existing designs necessitate the use of capacitors in the circuit to implement constraint functions, which limits the extent of latency reduction and still yields latency in the order of milliseconds due to the capacitor charging time. In [5], a discrete analog QP solver was implemented on a PCB, and a resistive crossbar array was adopted to solve the targeted cost function without additional capacitors and reduce the computing time to $80 \mu \mathrm{s}$. Nonetheless, the digital potentiometers used in the design had poor linearity and hence degraded solution accuracy. Furthermore, the use of bulky digital potentiometers and excessive power/area consumption prevent the solver from real-time power/area-constrained applications where high accuracy, low latency, and low cost are needed.

This letter presents a novel Adaptive-frequency MPC framework for bipedal locomotion over terrain with uneven stepping stones. In detail, we intend to achieve adaptive gait periods with variable MPC frequency for bipedal periodic walking gait to traverse terrain with discontinuities without slowing down. We pair this adaptive-frequency MPC with kino-dynamics trajectory optimization to obtain MPC adaptive frequencies (in terms of sampling times), center of mass (CoM) trajectory, and foot placements. We use whole-body control (WBC) along with adaptive-frequency MPC to track the optimal trajectories from offline optimization. In numerical validations, our adaptive-frequency optimization and MPC framework have shown advantages over fixed-frequency MPC. The proposed framework can control the bipedal robot to traverse through uneven stepping stone terrains with perturbed stone heights, widths, and surface shapes while maintaining an average speed of 1.5 m/s.