5 citations · 10 across the 9 of their papers we have counts for
10 papers · 1 filter
OIL: An Object Interaction Layer for Humanoid Whole-Body Control
Pranay Dugar, Mohitvishnu S. Gadde, Rajesh Mangannavar +2
Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint or end-effector trajectories.…
Learning Multi-Humanoid Pickup and Transport via Decentralized Object-Centric Control
Bikram Pandit, Mohitvishnu S. Gadde, Aayam Kumar Shrestha +1
We study cooperative multi-humanoid pickup and transport of objects with varying size, weight, and geometry, requiring robot teams of different sizes. Our approach uses decentraliz…
Collision-Aware Humanoid Whole-Body Control under Imperfect Tracking Targets
Mohitvishnu S. Gadde, Ashish Malik, Pranay Dugar +3
Humanoid robots often execute motion commands through whole-body controllers (WBCs) that track targets while maintaining balance and stability. However, most WBCs are blind to scen…
Humanoid Hanoi: Investigating Shared Whole-Body Control for Skill-Based Box Rearrangement
Minku Kim, Kuan-Chia Chen, Aayam Shrestha +3
We introduce \emph{Humanoid Hanoi}, a challenge problem for long-horizon humanoid skill composition that requires a robot to repeatedly sequence pickup, locomotion, and placement s…
Multi-Quadruped Cooperative Object Transport: Learning Decentralized Pinch-Lift-Move
Bikram Pandit, Aayam Kumar Shrestha, Alan Fern
We study decentralized cooperative transport using teams of N-quadruped robots with arm that must pinch, lift, and move ungraspable objects through physical contact alone. Unlike p…
No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Pranay Dugar, Mohitvishnu S. Gadde, Jonah Siekmann +3
Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical, these transitions must be fast, robu…
Generating Physically Realistic and Directable Human Motions from Multi-Modal Inputs
Aayam Shrestha, Pan Liu, German Ros +2
This work focuses on generating realistic, physically-based human behaviors from multi-modal inputs, which may only partially specify the desired motion. For example, the input may…
Learning Multi-Modal Whole-Body Control for Real-World Humanoid Robots
Pranay Dugar, Aayam Shrestha, Fangzhou Yu +2
A major challenge in humanoid robotics is designing a unified interface for commanding diverse whole-body behaviors, from precise footstep sequences to partial-body mimicry and joy…
Learning Decentralized Multi-Biped Control for Payload Transport
Bikram Pandit, Ashutosh Gupta, Mohitvishnu S. Gadde +5
Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectivene…
Revisiting Reward Design and Evaluation for Robust Humanoid Standing and Walking
Bart van Marum, Aayam Shrestha, Helei Duan +3
A necessary capability for humanoid robots is the ability to stand and walk while rejecting natural disturbances. Recent progress has been made using sim-to-real reinforcement lear…