6 citations · 10 across the 11 of their papers we have counts for
14 papers
ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction
Jinzhou Li, Hadi Tabatabaee, Kelin Yu +6
Dexterous manipulation policies trained in simulation often fail to transfer to the real world because of errors in contact timing and force regulation. Yet these policies can reta…
SimEX: Simulation-Integrated Robotics AutoResearch
Jiaheng Hu, Roberto Martin-Martin, Peter Stone +3
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this su…
OopsieVerse: A Safety Benchmark with Damage-Aware Simulation for Robot Manipulation
Arnav Balaji, Arpit Bahety, Sriniket Ambatipudi +3
While robotic manipulation capabilities have advanced rapidly, physical safety remains a major barrier to deploying household robots: task success is insufficient if the robot dama…
CoDex: Learning Compositional Dexterous Functional Manipulation without Demonstrations
Bowen Jiang, William Painter Reger, Roberto Martin-Martin
In this work, we study Compositional Dexterous Functional Object Manipulation (CD-FOM): tasks such as aiming and actuating a spray bottle on a plant or a glue gun on wood, which re…
Learning to Look: Seeking Information for Decision Making via Policy Factorization
Shivin Dass, Jiaheng Hu, Ben Abbatematteo +2
Many robot manipulation tasks require active or interactive exploration behavior in order to be performed successfully. Such tasks are ubiquitous in embodied domains, where agents…
SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Zizhao Wang, Jiaheng Hu, Caleb Chuck +5
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised…
Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning
Jiaheng Hu, Zizhao Wang, Peter Stone +1
A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment. However, existing unsupervised skill discovery m…
KinScene: Model-Based Mobile Manipulation of Articulated Scenes
Cheng-Chun Hsu, Ben Abbatematteo, Zhenyu Jiang +3
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articu…
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes
Chen Tang, Ben Abbatematteo, Jiaheng Hu +3
Reinforcement learning (RL), particularly its combination with deep neural networks referred to as deep RL (DRL), has shown tremendous promise across a wide range of applications,…
Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation
Srinath Tankasala, Roberto Martín-Martín, Mitch Pryor
Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously coll…
Natural Language Can Help Bridge the Sim2Real Gap
Albert Yu, Adeline Foote, Raymond Mooney +1
The main challenge in learning image-conditioned robotic policies is acquiring a visual representation conducive to low-level control. Due to the high dimensionality of the image s…
ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
Arpit Bahety, Priyanka Mandikal, Ben Abbatematteo +1
Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate…
BaRiFlex: A Robotic Gripper with Versatility and Collision Robustness for Robot Learning
Gu-Cheol Jeong, Arpit Bahety, Gabriel Pedraza +2
We present a new approach to robot hand design specifically suited for successfully implementing robot learning methods to accomplish tasks in daily human environments. We introduc…
Causal Policy Gradient for Whole-Body Mobile Manipulation
Jiaheng Hu, Peter Stone, Roberto Martín-Martín
Developing the next generation of household robot helpers requires combining locomotion and interaction capabilities, which is generally referred to as mobile manipulation (MoMa).…