I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2405.08726v2
• Date: Mon, 17 Feb 2025 14:32:21 GMT
• Title: I-CTRL: Imitation to Control Humanoid Robots Through Constrained Reinforcement Learning
• Authors: Yashuai Yan, Esteve Valls Mascaro, Tobias Egle, Dongheui Lee,
• Abstract summary: We develop a framework to control humanoid robots through bounded residual reinforcement learning (I-CTRL)<n>I-CTRL excels in motion imitation with simple and unique rewards that generalize across five robots.<n>Our framework introduces an automatic priority scheduler to manage large-scale motion datasets.
• Score: 8.97654258232601
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humanoid robots have the potential to mimic human motions with high visual fidelity, yet translating these motions into practical, physical execution remains a significant challenge. Existing techniques in the graphics community often prioritize visual fidelity over physics-based feasibility, posing a significant challenge for deploying bipedal systems in practical applications. This paper addresses these issues through bounded residual reinforcement learning to produce physics-based high-quality motion imitation onto legged humanoid robots that enhance motion resemblance while successfully following the reference human trajectory. Our framework, Imitation to Control Humanoid Robots Through Bounded Residual Reinforcement Learning (I-CTRL), reformulates motion imitation as a constrained refinement over non-physics-based retargeted motions. I-CTRL excels in motion imitation with simple and unique rewards that generalize across five robots. Moreover, our framework introduces an automatic priority scheduler to manage large-scale motion datasets when efficiently training a unified RL policy across diverse motions. The proposed approach signifies a crucial step forward in advancing the control of bipedal robots, emphasizing the importance of aligning visual and physical realism for successful motion imitation.

Related papers

• Reinforcement learning-based motion imitation for physiologically plausible musculoskeletal motor control [47.423243831156285]
  We present a model-free motion imitation framework (KINESIS) to advance the understanding of muscle-based motor control. We demonstrate that KINESIS achieves strong imitation performance on 1.9 hours of motion capture data. KINESIS generates muscle activity patterns that correlate well with human EMG activity.
  arXiv  Detail & Related papers  (2025-03-18T18:37:49Z)
• Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
  Existing approaches learn a policy to predict a distant next-best end-effector pose. They then compute the corresponding joint rotation angles for motion using inverse kinematics. We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
  arXiv  Detail & Related papers  (2025-03-13T17:48:35Z)
• Aligning Human Motion Generation with Human Perceptions [51.831338643012444]
  We propose a data-driven approach to bridge the gap by introducing a large-scale human perceptual evaluation dataset, MotionPercept, and a human motion critic model, MotionCritic. Our critic model offers a more accurate metric for assessing motion quality and could be readily integrated into the motion generation pipeline.
  arXiv  Detail & Related papers  (2024-07-02T14:01:59Z)
• DrEureka: Language Model Guided Sim-To-Real Transfer [64.14314476811806]
  Transferring policies learned in simulation to the real world is a promising strategy for acquiring robot skills at scale. In this paper, we investigate using Large Language Models (LLMs) to automate and accelerate sim-to-real design. Our approach is capable of solving novel robot tasks, such as quadruped balancing and walking atop a yoga ball.
  arXiv  Detail & Related papers  (2024-06-04T04:53:05Z)
• Real-Time Dynamic Robot-Assisted Hand-Object Interaction via Motion Primitives [45.256762954338704]
  We propose an approach to enhancing physical HRI with a focus on dynamic robot-assisted hand-object interaction. We employ a transformer-based algorithm to perform real-time 3D modeling of human hands from single RGB images. The robot's action implementation is dynamically fine-tuned using the continuously updated 3D hand models.
  arXiv  Detail & Related papers  (2024-05-29T21:20:16Z)
• Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers [19.052211315080044]
  Bimanual handovers are crucial for transferring large, deformable or delicate objects. This paper proposes a framework for generating kinematically constrained human-like bimanual robot motions.
  arXiv  Detail & Related papers  (2024-02-22T13:19:02Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• DROP: Dynamics Responses from Human Motion Prior and Projective Dynamics [21.00283279991885]
  We introduce DROP, a novel framework for modeling Dynamics Responses of humans using generative mOtion prior and Projective dynamics. We conduct extensive evaluations of our model across different motion tasks and various physical perturbations, demonstrating the scalability and diversity of responses.
  arXiv  Detail & Related papers  (2023-09-24T20:25:59Z)
• Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives [1.7901837062462316]
  We introduce a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the Dynamic Movement Primitives framework. Our method was implemented into an actual human-robot setup to extract human dynamic features and used to regenerate the robot trajectories following both LfD and RL.
  arXiv  Detail & Related papers  (2023-04-12T08:48:28Z)
• Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
  We propose the first framework to learn control policies for vision-based human-to-robot handovers. We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
  arXiv  Detail & Related papers  (2023-03-30T17:58:36Z)
• Learning Bipedal Walking for Humanoids with Current Feedback [5.429166905724048]
  We present an approach for overcoming the sim2real gap issue for humanoid robots arising from inaccurate torque-tracking at the actuator level. Our approach successfully trains a unified, end-to-end policy in simulation that can be deployed on a real HRP-5P humanoid robot to achieve bipedal locomotion.
  arXiv  Detail & Related papers  (2023-03-07T08:16:46Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• Physics-based Human Motion Estimation and Synthesis from Videos [0.0]
  We propose a framework for training generative models of physically plausible human motion directly from monocular RGB videos. At the core of our method is a novel optimization formulation that corrects imperfect image-based pose estimations. Results show that our physically-corrected motions significantly outperform prior work on pose estimation.
  arXiv  Detail & Related papers  (2021-09-21T01:57:54Z)
• UniCon: Universal Neural Controller For Physics-based Character Motion [70.45421551688332]
  We propose a physics-based universal neural controller (UniCon) that learns to master thousands of motions with different styles by learning on large-scale motion datasets. UniCon can support keyboard-driven control, compose motion sequences drawn from a large pool of locomotion and acrobatics skills and teleport a person captured on video to a physics-based virtual avatar.
  arXiv  Detail & Related papers  (2020-11-30T18:51:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.