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Generalizable Humanoid Manipulation with 3D Diffusion Policies
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Humanoid robots capable of autonomous operation in diverse environments have long been a goal for roboticists. However, autonomous manipulation by humanoid robots has largely been restricted to one specific scene, primarily due to the difficulty of acquiring generalizable skills and the expensiveness of in-the-wild humanoid robot data. In this work, we build a real-world robotic system to address this challenging problem. Our system is mainly an integration of 1) a whole-upper-body robotic teleoperation system to acquire human-like robot data, 2) a 25-DoF humanoid robot platform with a height-adjustable cart and a 3D LiDAR sensor, and 3) an improved 3D Diffusion Policy learning algorithm for humanoid robots to learn from noisy human data. We run more than 2000 episodes of policy rollouts on the real robot for rigorous policy evaluation. Empowered by this system, we show that using only data collected in one single scene and with only onboard computing, a full-sized humanoid robot can autonomously perform skills in diverse real-world scenarios. Videos are available at https://humanoid-manipulation.github.io .
Forward citations
Cited by 13 Pith papers
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StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision
A vision-language-action model that fuses stereo-derived geometric features with semantic features improves real-world grasping success and camera-pose robustness over single-view baselines.
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Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
A 10,300-demonstration, 260-task multimodal humanoid manipulation dataset with baseline policy evaluations and a cloud evaluation platform.
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UMI-on-Air: Embodiment-Aware Guidance for Embodiment-Agnostic Visuomotor Policies
Embodiment-Aware Diffusion Policy steers a UMI-trained diffusion policy with controller tracking-cost gradients at inference time, improving aerial manipulation success in simulation and real flights.
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Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Gaze-guided foveated patch tokenization reduces ViT tokens by 94%, accelerates training 7x and inference 3x, and improves robustness to distractors in bimanual manipulation policies.
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TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
A type-guided teleoperation system that selects predefined dexterous hand poses with a language model outperforms retargeting-based teleoperation on nine real-world tasks and improves imitation learning success.
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Vision in Action: Learning Active Perception from Human Demonstrations
ViA trains bimanual manipulation policies from human demonstrations that include active head-camera movement, using a 6-DoF robot neck and a VR interface with point-cloud rendering, reporting large gains on three occl...
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Versatile Loco-Manipulation through Flexible Interlimb Coordination
ReLIC lets a robot dog dynamically reassign its legs between walking and manipulating, achieving 78.9% average success across 12 real-world loco-manipulation tasks.
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4D Visual Pre-training for Robot Learning
A next-frame point-cloud diffusion pre-training method (FVP) improves DP3 and RDT-1B manipulation success rates on the paper's own tasks.
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Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
The claimed result is that source-component-shift adaptation splits cleanly into offline component learning via EM and online mixing-weight updates, cutting cumulative test loss by up to 67.4%.
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Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
A humanoid-specific multimodal occupancy perception system with a new dataset, sensor layout, and a fusion network that claims state-of-the-art results on its own benchmark.
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MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
MoTo turns existing fixed-base manipulation models into mobile manipulators by using VLM-picked contact keypoints and trajectory optimization to find docking points, with no training of MoTo itself.
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Leveraging OS-Level Primitives for Robotic Action Management
Applying OS-style exception handling, context caching, and replay to robotic action slices raises success rates 7x to 24x and cuts execution steps up to 74% for repetitive manipulation tasks, without retraining the VLA model.
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A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Embodied intelligence learning is reviewed through the complementary lenses of physical simulators and world models, with a proposed IR-L0 to IR-L4 robot capability taxonomy.
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