1 citations · 2 across the 10 of their papers we have counts for
11 papers
Dense Temporal Motion Retargeting for Legged Robots
Jaeryeong Kim, Taerim Yoon, Jin Cheng +2
Legged robots can learn expressive whole-body skills from the motions of humans and animals. Due to the morphology gap between the source and the robot, however, the motion must be…
Teaching Robots Like Dogs: Learning Agile Navigation from Luring, Gesture, and Speech
Taerim Yoon, Dongho Kang, Jin Cheng +5
In this work, we aim to enable legged robots to learn how to interpret human social cues and produce appropriate behaviors through physical human guidance. However, learning throug…
Whole-Body Inverse Dynamics MPC for Legged Loco-Manipulation
Lukas Molnar, Jin Cheng, Gabriele Fadini +3
Loco-manipulation demands coordinated whole-body motion to manipulate objects effectively while maintaining locomotion stability, presenting significant challenges for both plannin…
Switch-JustDance: Benchmarking Whole Body Motion Tracking Controllers Using a Commercial Console Game
Jeonghwan Kim, Wontaek Kim, Yidan Lu +9
Recent advances in whole-body robot control have enabled humanoid and legged robots to perform increasingly agile and coordinated motions. However, standardized benchmarks for eval…
TARC: Time-Adaptive Robotic Control
Arnav Sukhija, Lenart Treven, Jin Cheng +3
Most robotic systems rely on fixed-frequency discrete-time controllers, creating a trade-off between the efficiency of low-frequency control and the responsiveness of high-frequenc…
Walk Like Dogs: Learning Steerable Imitation Controllers for Legged Robots from Unlabeled Motion Data
Dongho Kang, Jin Cheng, Fatemeh Zargarbashi +3
We present an imitation learning framework that extracts distinctive legged locomotion behaviors and transitions between them from unlabeled real-world motion data. By automaticall…
RAMBO: RL-Augmented Model-Based Whole-Body Control for Loco-Manipulation
Jin Cheng, Dongho Kang, Gabriele Fadini +2
Loco-manipulation, physical interaction of various objects that is concurrently coordinated with locomotion, remains a major challenge for legged robots due to the need for both pr…
CAIMAN: Causal Action Influence Detection for Sample-efficient Loco-manipulation
Yuanchen Yuan, Jin Cheng, Núria Armengol Urpí +1
Enabling legged robots to perform non-prehensile loco-manipulation is crucial for enhancing their versatility. Learning behaviors such as whole-body object pushing often requires s…
Learning More With Less: Sample Efficient Model-Based RL for Loco-Manipulation
Benjamin Hoffman, Jin Cheng, Chenhao Li +1
By combining the agility of legged locomotion with the capabilities of manipulation, loco-manipulation platforms have the potential to perform complex tasks in real-world applicati…
MetaLoco: Universal Quadrupedal Locomotion with Meta-Reinforcement Learning and Motion Imitation
Fatemeh Zargarbashi, Fabrizio Di Giuro, Jin Cheng +3
This work presents a meta-reinforcement learning approach to develop a universal locomotion control policy capable of zero-shot generalization across diverse quadrupedal platforms.…
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
Fatemeh Zargarbashi, Jin Cheng, Dongho Kang +2
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level obj…