CONTACT: CONtact-aware TACTile Learning for Robotic Disassembly

• URL: http://arxiv.org/abs/2603.08560v1
• Date: Mon, 09 Mar 2026 16:20:49 GMT
• Title: CONTACT: CONtact-aware TACTile Learning for Robotic Disassembly
• Abstract summary: We investigate the role of tactile sensing in robotic disassembly through both simulation and real-world experiments.<n>We compare three sensing configurations: Vision Only, Vision + tactile RGB (TacRGB), and Vision + tactile force field (TacFF)<n>Our results show that tactile sensing plays a critical, task-dependent role in robotic disassembly, with structured force-field representations being particularly effective in contact-dominated scenarios.
• Score: 5.119250053135017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic disassembly involves contact-rich interactions in which successful manipulation depends not only on geometric alignment but also on force-dependent state transitions. While vision-based policies perform well in structured settings, their reliability often degrades in tight-tolerance, contact-dominated, or deformable scenarios. In this work, we systematically investigate the role of tactile sensing in robotic disassembly through both simulation and real-world experiments. We construct five rigid-body disassembly tasks in simulation with increasing geometric constraints and extraction difficulty. We further design five real-world tasks, including three rigid and two deformable scenarios, to evaluate contact-dependent manipulation. Within a unified learning framework, we compare three sensing configurations: Vision Only, Vision + tactile RGB (TacRGB), and Vision + tactile force field (TacFF). Across both simulation and real-world experiments, TacFF-based policies consistently achieve the highest success rates, with particularly notable gains in contact-dependent and deformable settings. Notably, naive fusion of TacRGB and TacFF underperforms either modality alone, indicating that simple concatenation can dilute task-relevant force information. Our results show that tactile sensing plays a critical, task-dependent role in robotic disassembly, with structured force-field representations being particularly effective in contact-dominated scenarios.

Related papers

• Learning Physical Interaction: A Survey of Tactile- and Force-aware Robot Learning [128.76135832773102]
  Physically grounded robot intelligence requires robots to perceive, reason about, and regulate their interactions with the physical world.<n>Recent robot learning methods have made substantial progress by integrating force, tactile, vision, language, and proprioceptive sensing into learned manipulation policies.<n>Despite these advances, existing surveys have not explicitly reviewed force- and tactile-aware robot learning from a unified perspective.
  arXiv  Detail & Related papers  (2026-08-02T14:22:01Z)
• TACTIC: Tactile and Vision Conditioned Contact-Centric Control for Whole-Arm Manipulation [3.8628620899766553]
  Whole-arm manipulation involves direct contact with the environment while the robot completes a task.<n>We propose TACTIC (Tactile and Vision Conditioned Contact-Centric Control), a receding-horizon controller for whole-arm manipulation.
  arXiv  Detail & Related papers  (2026-07-10T09:08:55Z)
• Beyond Binary: Sim-to-Real Dexterous Manipulation with Physics-Grounded Contact Representation [51.463992799813816]
  A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data.<n>We introduce Center-of-Pressure, an effective tactile representation grounded in physical principles.<n>We evaluate CoP on two blind, challenging contact-rich manipulation tasks.
  arXiv  Detail & Related papers  (2026-05-27T17:59:02Z)
• Learning Tactile-Aware Quadrupedal Loco-Manipulation Policies [33.25194785586049]
  We present a tactile-aware loco-manipulation policy learning pipeline with a hierarchical structure.<n>First, we leverage real-world human demonstrations to train a tactile-conditioned visuotactile high-level policy.<n>Second, we perform large-scale reinforcement learning in simulation to learn a tactile-aware whole-body control policy.
  arXiv  Detail & Related papers  (2026-04-29T21:46:58Z)
• Direction Matters: Learning Force Direction Enables Sim-to-Real Contact-Rich Manipulation [28.707088673008172]
  We propose a framework that leverages expert-designed controller logic for transfer.<n>Inspired by the success of privileged supervision in tasks, we employ a human-designed finite state machine based position/force controller in simulation.<n>Unlike force magnitudes, which are highly sensitive to simulation inaccuracies, force directions encode high-level task geometry and remain robust across the sim-to-real gap.
  arXiv  Detail & Related papers  (2026-02-15T14:57:13Z)
• Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation [82.63833405368159]
  Generalizing tool manipulation requires both semantic planning and precise physical control.<n>We propose Semantic-Contact Fields (SCFields), a unified 3D representation fusing visual semantics with dense contact estimates.<n> Experiments on scraping, crayon drawing, and peeling demonstrate robust category-level generalization.
  arXiv  Detail & Related papers  (2026-02-14T16:05:08Z)
• Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist [10.982180941605256]
  We introduce Tactile Memory with Soft Robot (TaSo-bot), a system that integrates a soft wrist with retrieval-based control to enable safe and robust manipulation.<n>The core of this system is the Masked Tactile Trajectory Transformer (MATtext3$), which jointly models interactions between robot actions, tactile feedback, force-torque measurements, and proprioceptive signals.<n>MATtext3$ achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.
  arXiv  Detail & Related papers  (2026-01-27T07:04:01Z)
• Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation [21.78866976181311]
  See-through-skin (STS) sensors combine tactile and visual perception.<n>Existing STS designs lack simultaneous multimodal perception and suffer from unreliable tactile tracking.<n>We introduce TacThru, an STS sensor enabling simultaneous visual perception and robust tactile signal extraction.
  arXiv  Detail & Related papers  (2025-12-10T17:35:13Z)
• ForceGrip: Reference-Free Curriculum Learning for Realistic Grip Force Control in VR Hand Manipulation [0.10995326465245926]
  We present ForceGrip, a deep learning agent that synthesizes realistic hand manipulation motions.<n>We employ a three-phase curriculum learning framework comprising Finger Positioning, Intention Adaptation, and Dynamic Stabilization.<n>Our evaluations reveal ForceGrip's superior force controllability and plausibility compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-03-11T05:39:07Z)
• Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation [58.95799126311524]
  Humans can accomplish contact-rich tasks using vision and touch, with highly reactive capabilities such as fast response to external changes and adaptive control of contact forces.<n>Existing visual imitation learning approaches rely on action chunking to model complex behaviors.<n>We introduce TactAR, a low-cost teleoperation system that provides real-time tactile feedback through Augmented Reality.
  arXiv  Detail & Related papers  (2025-03-04T18:58:21Z)
• Twisting Lids Off with Two Hands [82.21668778600414]
  We show how policies trained in simulation can be effectively and efficiently transferred to the real world. Specifically, we consider the problem of twisting lids of various bottle-like objects with two hands. This is the first sim-to-real RL system that enables such capabilities on bimanual multi-fingered hands.
  arXiv  Detail & Related papers  (2024-03-04T18:59:30Z)
• Towards Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot [19.789369416528604]
  grasp force control aims to manipulate objects safely by limiting the amount of force exerted on the object. Prior works have either hand-modeled their force controllers, employed model-based approaches, or have not shown sim-to-real transfer. We propose a model-free deep reinforcement learning approach trained in simulation and then transferred to the robot without further fine-tuning.
  arXiv  Detail & Related papers  (2023-11-13T11:29:06Z)
• Robotic Handling of Compliant Food Objects by Robust Learning from Demonstration [79.76009817889397]
  We propose a robust learning policy based on Learning from Demonstration (LfD) for robotic grasping of food compliant objects. We present an LfD learning policy that automatically removes inconsistent demonstrations, and estimates the teacher's intended policy. The proposed approach has a vast range of potential applications in the aforementioned industry sectors.
  arXiv  Detail & Related papers  (2023-09-22T13:30:26Z)
• Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning [24.223788665601678]
  Two xArm6 robots solve the U-shape assembly task with a success rate of above90% in simulation, and 50% on real hardware without any additional real-world fine-tuning. Our results present a significant step forward for bi-arm capability on real hardware, and we hope our system can inspire future research on deep RL and Sim2Real transfer bi-manualpolicies.
  arXiv  Detail & Related papers  (2023-03-27T01:25:24Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Elastic Tactile Simulation Towards Tactile-Visual Perception [58.44106915440858]
  We propose Elastic Interaction of Particles (EIP) for tactile simulation. EIP models the tactile sensor as a group of coordinated particles, and the elastic property is applied to regulate the deformation of particles during contact. We further propose a tactile-visual perception network that enables information fusion between tactile data and visual images.
  arXiv  Detail & Related papers  (2021-08-11T03:49:59Z)

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.