DreamControl-v2: Simpler and Scalable Autonomous Humanoid Skills via Trainable Guided Diffusion Priors

• URL: http://arxiv.org/abs/2604.00202v1
• Date: Tue, 31 Mar 2026 20:11:49 GMT
• Title: DreamControl-v2: Simpler and Scalable Autonomous Humanoid Skills via Trainable Guided Diffusion Priors
• Abstract summary: We propose an improved framework that trains a guided diffusion model directly in the humanoid robot's motion space.<n>We demonstrate that our approach captures a wider range of skills due to the larger training data mixture.<n>We show that scaling the generation of reference trajectories is important for achieving robust downstream RL policies.
• Score: 20.558671221723422
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Developing robust autonomous loco-manipulation skills for humanoids remains an open problem in robotics. While RL has been applied successfully to legged locomotion, applying it to complex, interaction-rich manipulation tasks is harder given long-horizon planning challenges for manipulation. A recent approach along these lines is DreamControl, which addresses these issues by leveraging off-the-shelf human motion diffusion models as a generative prior to guide RL policies during training. In this paper, we investigate the impact of DreamControl's motion prior and propose an improved framework that trains a guided diffusion model directly in the humanoid robot's motion space, aggregating diverse human and robot datasets into a unified embodiment space. We demonstrate that our approach captures a wider range of skills due to the larger training data mixture and establishes a more automated pipeline by removing the need for manual filtering interventions. Furthermore, we show that scaling the generation of reference trajectories is important for achieving robust downstream RL policies. We validate our approach through extensive experiments in simulation and on a real Unitree-G1.

Related papers

• BifrostUMI: Bridging Robot-Free Demonstrations and Humanoid Whole-Body Manipulation [6.733928872257592]
  BifrostUMI is a portable, efficient, and robot-free data collection framework for humanoid robots.<n>We demonstrate the efficacy and versatility of the proposed framework across two distinct experimental scenarios.
  arXiv  Detail & Related papers  (2026-05-05T07:35:09Z)
• ZeroWBC: Learning Natural Visuomotor Humanoid Control Directly from Human Egocentric Video [52.78703020909145]
  We introduce ZeroWBC, a novel framework that learns a natural humanoid visuomotor control policy directly from human egocentric videos.<n>Our approach first fine-tunes a Vision-Language Model (VLM) to predict future whole-body human motions based on text instructions and egocentric visual context.<n>Experiments on the Unitree G1 humanoid robot demonstrate that our method outperforms baseline approaches in motion naturalness and versatility.
  arXiv  Detail & Related papers  (2026-03-10T04:19:43Z)
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration [67.13034606664333]
  EgoHumanoid is the first framework to co-train a vision-language-action policy using egocentric human demonstrations.<n>A portable system for scalable human data collection is developed.
  arXiv  Detail & Related papers  (2026-02-10T18:59:03Z)
• DreamControl: Human-Inspired Whole-Body Humanoid Control for Scene Interaction via Guided Diffusion [21.619868038556987]
  We introduce DreamControl, a novel methodology for learning autonomous whole-body humanoid skills.<n>Our core innovation is the use of a diffusion prior trained on human motion data, which guides an RL policy in simulation to complete specific tasks of interest.<n>We validate DreamControl's effectiveness on a Unitree G1 robot across a diverse set of challenging tasks involving simultaneous lower and upper body control and object interaction.
  arXiv  Detail & Related papers  (2025-09-17T18:35:43Z)
• Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
  Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
  arXiv  Detail & Related papers  (2025-09-11T17:59:07Z)
• Trajectory Adaptation using Large Language Models [0.8704964543257245]
  Adapting robot trajectories based on human instructions as per new situations is essential for achieving more intuitive and scalable human-robot interactions.<n>This work proposes a flexible language-based framework to adapt generic robotic trajectories produced by off-the-shelf motion planners.<n>We utilize pre-trained LLMs to adapt trajectory waypoints by generating code as a policy for dense robot manipulation.
  arXiv  Detail & Related papers  (2025-04-17T08:48:23Z)
• RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model [13.110235244912474]
  Redundant manipulators offer enhanced kinematic performance and versatility.<n>Motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments.<n>This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
  arXiv  Detail & Related papers  (2024-12-27T07:34:54Z)
• Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control [106.32794844077534]
  This paper presents a study on using deep reinforcement learning to create dynamic locomotion controllers for bipedal robots. We develop a general control solution that can be used for a range of dynamic bipedal skills, from periodic walking and running to aperiodic jumping and standing. This work pushes the limits of agility for bipedal robots through extensive real-world experiments.
  arXiv  Detail & Related papers  (2024-01-30T10:48:43Z)
• 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)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation [99.2543521972137]
  ReLMoGen is a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. ReLMoGen shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots.
  arXiv  Detail & Related papers  (2020-08-18T08:05:15Z)

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.