2 citations · 9 across the 32 of their papers we have counts for
34 papers
The contraction of solutions with large perturbation in multiple space dimensions from the oscillatory dispersive planar shock
Geng Chen, Harish Ravichandar, Yannan Shen
In this paper, we show the contraction property of the planar oscillatory or monotone dispersive shock profiles of the dissipative Kadomtsev-Petviashvili (KP) equation modell…
OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
Kelin Yu, Haode Zhang, Harish Ravichandar +2
Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success signific…
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Yunhai Han, Jianuo Qiu, Linhao Bai +14
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception e…
Distributionally Robust Control via Stein Variational Inference for Contact-Rich Manipulation
Hrishikesh Sathyanarayan, Victor Vantilborgh, Harish Ravichandar +2
Reliable robotic manipulation requires control policies that can accurately represent and adapt to uncertainty arising from contact-rich interactions. Modern data-driven methods mi…
Modeling and Optimizing the Provisioning of Exhaustible Capabilities for Simultaneous Task Allocation and Scheduling
Jinwoo Park, Harish Ravichandar, Seth Hutchinson
Deploying heterogeneous robot teams to accomplish multiple tasks over extended time horizons presents significant computational challenges for task allocation and planning. In this…
Learning and Optimizing the Efficacy of Spatio-Temporal Task Allocation under Temporal and Resource Constraints
Jiazhen Liu, Glen Neville, Jinwoo Park +2
Complex multi-robot missions often require heterogeneous teams to jointly optimize task allocation, scheduling, and path planning to improve team performance under strict constrain…
Towards Automated Chicken Deboning via Learning-based Dynamically-Adaptive 6-DoF Multi-Material Cutting
Zhaodong Yang, Ai-Ping Hu, Harish Ravichandar
Automating chicken shoulder deboning requires precise 6-DoF cutting through a partially occluded, deformable, multi-material joint, since contact with the bones presents serious he…
ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
Yangcen Liu, Woo Chul Shin, Yunhai Han +3
Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, an…
JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes
Shalin Anand Jain, Jiazhen Liu, Siva Kailas +1
Multi-agent reinforcement learning (MARL) has emerged as a promising solution for learning complex and scalable coordination behaviors in multi-robot systems. However, established…
Evaluating and Improving Graph-based Explanation Methods for Multi-Agent Coordination
Siva Kailas, Shalin Jain, Harish Ravichandar
Graph Neural Networks (GNNs), developed by the graph learning community, have been adopted and shown to be highly effective in multi-robot and multi-agent learning. Inspired by thi…
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot Coordination
Kevin Fu, Shalin Anand Jain, Pierce Howell +1
Recent advances have enabled heterogeneous multi-robot teams to learn complex and effective coordination skills. However, existing neural architectures that support heterogeneous t…
On the Surprising Effectiveness of Spectral Clipping in Learning Stable Linear and Latent-Linear Dynamical Systems
Hanyao Guo, Yunhai Han, Harish Ravichandar
When learning stable linear dynamical systems from data, three important properties are desirable: i) predictive accuracy, ii) verifiable stability, and iii) computational efficien…
AsymDex: Asymmetry and Relative Coordinates for RL-based Bimanual Dexterity
Zhaodong Yang, Yunhai Han, Ai-Ping Hu +1
We present Asymmetric Dexterity (AsymDex), a novel and simple reinforcement learning (RL) framework that can efficiently learn a large class of bimanual skills in multi-fingered ha…
Safe Navigation of Bipedal Robots via Koopman Operator-Based Model Predictive Control
Jeonghwan Kim, Yunhai Han, Harish Ravichandar +1
Nonlinearity in dynamics has long been a major challenge in robotics, often causing significant performance degradation in existing control algorithms. For example, the navigation…
KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation
Hongyi Chen, Abulikemu Abuduweili, Aviral Agrawal +4
Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Ko…
Q-ITAGS: Quality-Optimized Spatio-Temporal Heterogeneous Task Allocation with a Time Budget
Glen Neville, Jiazhen Liu, Sonia Chernova +1
Complex multi-objective missions require the coordination of heterogeneous robots at multiple inter-connected levels, such as coalition formation, scheduling, and motion planning.…
Learning Prehensile Dexterity by Imitating and Emulating State-only Observations
Yunhai Han, Zhenyang Chen, Kyle A Williams +1
When human acquire physical skills (e.g., tennis) from experts, we tend to first learn from merely observing the expert. But this is often insufficient. We then engage in practice,…
Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities
Pierce Howell, Max Rudolph, Reza Torbati +2
Recent advances in multi-agent reinforcement learning (MARL) are enabling impressive coordination in heterogeneous multi-robot teams. However, existing approaches often overlook th…
The Effects of Robot Motion on Comfort Dynamics of Novice Users in Close-Proximity Human-Robot Interaction
Pierce Howell, Jack Kolb, Yifan Liu +1
Effective and fluent close-proximity human-robot interaction requires understanding how humans get habituated to robots and how robot motion affects human comfort. While prior work…
MARBLER: An Open Platform for Standardized Evaluation of Multi-Robot Reinforcement Learning Algorithms
Reza Torbati, Shubham Lohiya, Shivika Singh +2
Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in compl…
Concurrent Constrained Optimization of Unknown Rewards for Multi-Robot Task Allocation
Sukriti Singh, Anusha Srikanthan, Vivek Mallampati +1
Task allocation can enable effective coordination of multi-robot teams to accomplish tasks that are intractable for individual robots. However, existing approaches to task allocati…
Fast Anticipatory Motion Planning for Close-Proximity Human-Robot Interaction
Sam Scheele, Pierce Howell, Harish Ravichandar
Effective close-proximity human-robot interaction (CP-HRI) requires robots to be able to both efficiently perform tasks as well as adapt to human behavior and preferences. However,…
On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills
Yunhai Han, Mandy Xie, Ye Zhao +1
Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is l…
Inferring Implicit Trait Preferences for Task Allocation in Heterogeneous Teams
Vivek Mallampati, Harish Ravichandar
Task allocation in heterogeneous multi-agent teams often requires reasoning about multi-dimensional agent traits (i.e., capabilities) and the demands placed on them by tasks. Howev…