Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation

• URL: http://arxiv.org/abs/2409.14896v2
• Date: Tue, 16 Sep 2025 15:03:40 GMT
• Title: Built Different: Tactile Perception to Overcome Cross-Embodiment Capability Differences in Collaborative Manipulation
• Authors: William van den Bogert, Madhavan Iyengar, Nima Fazeli,
• Abstract summary: We investigate how tactile sensing can help bridge differences between robotic embodiments in the context of collaborative manipulation.<n>We present an approach that uses tactile sensors and behavior cloning to transfer policies from robots with these capabilities to those without.<n>We demonstrate this positive transfer on four different tactile-enabled embodiments using the same policy trained on force-controlled robot data.
• Score: 2.0318411357438086
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tactile sensing is a widely-studied means of implicit communication between robot and human. In this paper, we investigate how tactile sensing can help bridge differences between robotic embodiments in the context of collaborative manipulation. For a robot, learning and executing force-rich collaboration require compliance to human interaction. While compliance is often achieved with admittance control, many commercial robots lack the joint torque monitoring needed for such control. To address this challenge, we present an approach that uses tactile sensors and behavior cloning to transfer policies from robots with these capabilities to those without. We train a single policy that demonstrates positive transfer across embodiments, including robots without torque sensing. We demonstrate this positive transfer on four different tactile-enabled embodiments using the same policy trained on force-controlled robot data. Across multiple proposed metrics, the best performance came from a decomposed tactile shear-field representation combined with a pre-trained encoder, which improved success rates over alternative representations.

Related papers

• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
  hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-16T18:56:04Z)
• 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)
• Feel the Force: Contact-Driven Learning from Humans [52.36160086934298]
  Controlling fine-grained forces during manipulation remains a core challenge in robotics.<n>We present FeelTheForce, a robot learning system that models human tactile behavior to learn force-sensitive manipulation.<n>Our approach grounds robust low-level force control in scalable human supervision, achieving a 77% success rate across 5 force-sensitive manipulation tasks.
  arXiv  Detail & Related papers  (2025-06-02T17:57:52Z)
• Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation [49.03165169369552]
  By training a single policy across many different kinds of robots, a robot learning method can leverage much broader and more diverse datasets. We propose CrossFormer, a scalable and flexible transformer-based policy that can consume data from any embodiment. We demonstrate that the same network weights can control vastly different robots, including single and dual arm manipulation systems, wheeled robots, quadcopters, and quadrupeds.
  arXiv  Detail & Related papers  (2024-08-21T17:57:51Z)
• Controlling diverse robots by inferring Jacobian fields with deep networks [48.279199537720714]
  Mirroring the complex structures and diverse functions of natural organisms is a long-standing challenge in robotics.<n>We introduce a method that uses deep neural networks to map a video stream of a robot to its visuomotor Jacobian field.<n>Our approach achieves accurate closed-loop control and recovers the causal dynamic structure of each robot.
  arXiv  Detail & Related papers  (2024-07-11T17:55:49Z)
• Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System [5.497832119577795]
  dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors. Learning from Demonstrations offers an intuitive alternative, allowing robots to learn manipulations through observed actions.
  arXiv  Detail & Related papers  (2024-06-21T09:03:37Z)
• Pedipulate: Enabling Manipulation Skills using a Quadruped Robot's Leg [11.129918951736052]
  Legged robots have the potential to become vital in maintenance, home support, and exploration scenarios. In this work, we explore pedipulation - using the legs of a legged robot for manipulation.
  arXiv  Detail & Related papers  (2024-02-16T17:20:45Z)
• Robot Synesthesia: In-Hand Manipulation with Visuotactile Sensing [15.970078821894758]
  We introduce a system that leverages visual and tactile sensory inputs to enable dexterous in-hand manipulation. Robot Synesthesia is a novel point cloud-based tactile representation inspired by human tactile-visual synesthesia.
  arXiv  Detail & Related papers  (2023-12-04T12:35:43Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)
• Using Knowledge Representation and Task Planning for Robot-agnostic Skills on the Example of Contact-Rich Wiping Tasks [44.99833362998488]
  We show how a single robot skill that utilizes knowledge representation, task planning, and automatic selection of skill implementations can be executed in different contexts. We demonstrate how the skill-based control platform enables this with contact-rich wiping tasks on different robot systems.
  arXiv  Detail & Related papers  (2023-08-27T21:17:32Z)
• Tactile-Filter: Interactive Tactile Perception for Part Mating [54.46221808805662]
  Humans rely on touch and tactile sensing for a lot of dexterous manipulation tasks. vision-based tactile sensors are being widely used for various robotic perception and control tasks. We present a method for interactive perception using vision-based tactile sensors for a part mating task.
  arXiv  Detail & Related papers  (2023-03-10T16:27:37Z)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation [104.5440430194206]
  This work investigates the perception of object manipulations performed with a communicative intent by two robots. We designed the robots' movements to communicate carefulness or not during the transportation of objects.
  arXiv  Detail & Related papers  (2022-08-03T13:26:52Z)
• Body Gesture Recognition to Control a Social Robot [5.557794184787908]
  We propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body gestures to train our network.
  arXiv  Detail & Related papers  (2022-06-15T13:49:22Z)
• 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)
• A Transferable Legged Mobile Manipulation Framework Based on Disturbance Predictive Control [15.044159090957292]
  Legged mobile manipulation, where a quadruped robot is equipped with a robotic arm, can greatly enhance the performance of the robot. We propose a unified framework disturbance predictive control where a reinforcement learning scheme with a latent dynamic adapter is embedded into our proposed low-level controller.
  arXiv  Detail & Related papers  (2022-03-02T14:54:10Z)
• Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together [68.44697646919515]
  This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
  arXiv  Detail & Related papers  (2022-02-03T10:04:26Z)
• In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning [8.365690203298966]
  We report the successful execution of in-air knotting of rope using a dual-arm two-finger robot based on deep learning. A manual description of appropriate robot motions corresponding to all object states is difficult to be prepared in advance. We constructed a model that instructed the robot to perform bowknots and overhand knots based on two deep neural networks trained using the data gathered from its sensorimotor.
  arXiv  Detail & Related papers  (2021-03-17T02:11:58Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)
• Human Grasp Classification for Reactive Human-to-Robot Handovers [50.91803283297065]
  We propose an approach for human-to-robot handovers in which the robot meets the human halfway. We collect a human grasp dataset which covers typical ways of holding objects with various hand shapes and poses. We present a planning and execution approach that takes the object from the human hand according to the detected grasp and hand position.
  arXiv  Detail & Related papers  (2020-03-12T19:58:03Z)

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.