BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances

• URL: http://arxiv.org/abs/2604.23249v2
• Date: Mon, 04 May 2026 07:58:10 GMT
• Title: BridgeACT: Bridging Human Demonstrations to Robot Actions via Unified Tool-Target Affordances
• Abstract summary: We present BridgeACT, a framework that learns robotic manipulation directly from human videos without requiring robot demonstration data.<n>Our key idea is to model affordance as an embodiment-agnostic intermediate representation that bridges human demonstrations and robot actions.<n>The resulting affordances are mapped to robot actions through a grasping module and a lightweight closed-loop motion controller.
• Score: 11.402773793645244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning robot manipulation from human videos is appealing due to the scale and diversity of human demonstrations, but transferring such demonstrations to executable robot behavior remains challenging. Prior work either relies on robot data for downstream adaptation or learns affordance representations that remain at the perception level and do not directly support real-world execution. We present BridgeACT, an affordance-driven framework that learns robotic manipulation directly from human videos without requiring any robot demonstration data. Our key idea is to model affordance as an embodiment-agnostic intermediate representation that bridges human demonstrations and robot actions. BridgeACT decomposes manipulation into two complementary problems: where to grasp and how to move. To this end, BridgeACT first grounds task-relevant affordance regions in the current scene, and then predicts task-conditioned 3D motion affordances from human demonstrations. The resulting affordances are mapped to robot actions through a grasping module and a lightweight closed-loop motion controller, enabling direct deployment on real robots. In addition, we represent complex manipulation tasks as compositions of affordance operations, which allows a unified treatment of diverse tasks and object-to-object interactions. Experiments on real-world manipulation tasks show that BridgeACT outperforms prior baselines and generalizes to unseen objects, scenes, and viewpoints.

Related papers

• Human2Any: Human-to-Robot Transfer via Constraint-Aware Compositional Planning [17.90707618632327]
  We present Human2Any, a framework for learning reusable object-centric interaction priors from human videos.<n>We validate Human2Any across diverse manipulation settings, including real-world experiments on a Franka tabletop setup and an RBY-1 humanoid mobile robot.
  arXiv  Detail & Related papers  (2026-06-27T08:45:27Z)
• From Human Hands to Robot Arms: Manipulation Skills Transfer via Trajectory Alignment [36.08997778717271]
  Learning diverse manipulation skills for real-world robots is bottlenecked by reliance on costly and hard-to-scale teleoperated demonstrations.<n>We introduce Traj2Action, a novel framework that bridges this embodiment gap by using the 3D trajectory of the operational endpoint as a unified intermediate representation.<n>Our policy first learns to generate a coarse trajectory, which forms a high-level motion plan by leveraging both human and robot data.
  arXiv  Detail & Related papers  (2025-10-01T04:21:12Z)
• VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation [53.63540587160549]
  VidBot is a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos.<n> VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.
  arXiv  Detail & Related papers  (2025-03-10T10:04:58Z)
• 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.<n>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)
• Whole-Body Teleoperation for Mobile Manipulation at Zero Added Cost [8.71539730969424]
  MoMa-Teleop is a novel teleoperation method that infers end-effector motions from existing interfaces.<n>We demonstrate that our approach results in a significant reduction in task completion time across a variety of robots and tasks.
  arXiv  Detail & Related papers  (2024-09-23T15:09:45Z)
• Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation [65.46610405509338]
  We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation. Our framework,Track2Act predicts tracks of how points in an image should move in future time-steps based on a goal. We show that this approach of combining scalably learned track prediction with a residual policy enables diverse generalizable robot manipulation.
  arXiv  Detail & Related papers  (2024-05-02T17:56:55Z)
• Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations [66.47064743686953]
  Eye-in-hand cameras have shown promise in enabling greater sample efficiency and generalization in vision-based robotic manipulation. Videos of humans performing tasks, on the other hand, are much cheaper to collect since they eliminate the need for expertise in robotic teleoperation. In this work, we augment narrow robotic imitation datasets with broad unlabeled human video demonstrations to greatly enhance the generalization of eye-in-hand visuomotor policies.
  arXiv  Detail & Related papers  (2023-07-12T07:04:53Z)
• Learning Video-Conditioned Policies for Unseen Manipulation Tasks [83.2240629060453]
  Video-conditioned Policy learning maps human demonstrations of previously unseen tasks to robot manipulation skills. We learn our policy to generate appropriate actions given current scene observations and a video of the target task. We validate our approach on a set of challenging multi-task robot manipulation environments and outperform state of the art.
  arXiv  Detail & Related papers  (2023-05-10T16:25:42Z)
• 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)
• 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)

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.