ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
- URL: http://arxiv.org/abs/2405.03666v1
- Date: Mon, 6 May 2024 17:43:34 GMT
- Title: ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection
- Authors: Arpit Bahety, Priyanka Mandikal, Ben Abbatematteo, Roberto Martín-Martín,
- Abstract summary: Humans learn bimanual manipulation skills by watching other humans and by refining their abilities through play.
Inspired by seminal work in psychology and biomechanics, we propose modeling the interaction between two hands as a serial kinematic linkage.
We introduce ScrewMimic, a framework that leverages this novel action representation to facilitate learning from human demonstration and self-supervised policy fine-tuning.
- Score: 12.630451735872144
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Bimanual manipulation is a longstanding challenge in robotics due to the large number of degrees of freedom and the strict spatial and temporal synchronization required to generate meaningful behavior. Humans learn bimanual manipulation skills by watching other humans and by refining their abilities through play. In this work, we aim to enable robots to learn bimanual manipulation behaviors from human video demonstrations and fine-tune them through interaction. Inspired by seminal work in psychology and biomechanics, we propose modeling the interaction between two hands as a serial kinematic linkage -- as a screw motion, in particular, that we use to define a new action space for bimanual manipulation: screw actions. We introduce ScrewMimic, a framework that leverages this novel action representation to facilitate learning from human demonstration and self-supervised policy fine-tuning. Our experiments demonstrate that ScrewMimic is able to learn several complex bimanual behaviors from a single human video demonstration, and that it outperforms baselines that interpret demonstrations and fine-tune directly in the original space of motion of both arms. For more information and video results, https://robin-lab.cs.utexas.edu/ScrewMimic/
Related papers
- You Only Teach Once: Learn One-Shot Bimanual Robotic Manipulation from Video Demonstrations [38.835807227433335]
Bimanual robotic manipulation is a long-standing challenge of embodied intelligence.
We propose YOTO, which can extract and then inject patterns of bimanual actions from as few as a single binocular observation.
YOTO achieves impressive performance in mimicking 5 intricate long-horizon bimanual tasks.
arXiv Detail & Related papers (2025-01-24T03:26:41Z) - Learning to Transfer Human Hand Skills for Robot Manipulations [12.797862020095856]
We present a method for teaching dexterous manipulation tasks to robots from human hand motion demonstrations.
Our approach learns a joint motion manifold that maps human hand movements, robot hand actions, and object movements in 3D, enabling us to infer one motion from others.
arXiv Detail & Related papers (2025-01-07T22:33:47Z) - Zero-Shot Robot Manipulation from Passive Human Videos [59.193076151832145]
We develop a framework for extracting agent-agnostic action representations from human videos.
Our framework is based on predicting plausible human hand trajectories.
We deploy the trained model zero-shot for physical robot manipulation tasks.
arXiv Detail & Related papers (2023-02-03T21:39:52Z) - Cross-Domain Transfer via Semantic Skill Imitation [49.83150463391275]
We propose an approach for semantic imitation, which uses demonstrations from a source domain, e.g. human videos, to accelerate reinforcement learning (RL)
Instead of imitating low-level actions like joint velocities, our approach imitates the sequence of demonstrated semantic skills like "opening the microwave" or "turning on the stove"
arXiv Detail & Related papers (2022-12-14T18:46:14Z) - Learning Reward Functions for Robotic Manipulation by Observing Humans [92.30657414416527]
We use unlabeled videos of humans solving a wide range of manipulation tasks to learn a task-agnostic reward function for robotic manipulation policies.
The learned rewards are based on distances to a goal in an embedding space learned using a time-contrastive objective.
arXiv Detail & Related papers (2022-11-16T16:26:48Z) - Synthesis and Execution of Communicative Robotic Movements with
Generative Adversarial Networks [59.098560311521034]
We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects.
We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics.
We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
arXiv Detail & Related papers (2022-03-29T15:03:05Z) - DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from
Video [86.49357517864937]
We propose DexVIP, an approach to learn dexterous robotic grasping from human-object interaction videos.
We do this by curating grasp images from human-object interaction videos and imposing a prior over the agent's hand pose.
We demonstrate that DexVIP compares favorably to existing approaches that lack a hand pose prior or rely on specialized tele-operation equipment.
arXiv Detail & Related papers (2022-02-01T00:45:57Z) - A Differentiable Recipe for Learning Visual Non-Prehensile Planar
Manipulation [63.1610540170754]
We focus on the problem of visual non-prehensile planar manipulation.
We propose a novel architecture that combines video decoding neural models with priors from contact mechanics.
We find that our modular and fully differentiable architecture performs better than learning-only methods on unseen objects and motions.
arXiv Detail & Related papers (2021-11-09T18:39:45Z) - Video2Skill: Adapting Events in Demonstration Videos to Skills in an
Environment using Cyclic MDP Homomorphisms [16.939129935919325]
Video2Skill (V2S) attempts to extend this capability to artificial agents by allowing a robot arm to learn from human cooking videos.
We first use sequence-to-sequence Auto-Encoder style architectures to learn a temporal latent space for events in long-horizon demonstrations.
We then transfer these representations to the robotic target domain, using a small amount of offline and unrelated interaction data.
arXiv Detail & Related papers (2021-09-08T17:59:01Z) - DexMV: Imitation Learning for Dexterous Manipulation from Human Videos [11.470141313103465]
We propose a new platform and pipeline, DexMV, for imitation learning to bridge the gap between computer vision and robot learning.
We design a platform with: (i) a simulation system for complex dexterous manipulation tasks with a multi-finger robot hand and (ii) a computer vision system to record large-scale demonstrations of a human hand conducting the same tasks.
We show that the demonstrations can indeed improve robot learning by a large margin and solve the complex tasks which reinforcement learning alone cannot solve.
arXiv Detail & Related papers (2021-08-12T17:51:18Z)
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.