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ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface
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Tactile information plays a crucial role for humans and robots to interact effectively with their environment, particularly for tasks requiring the understanding of contact properties. Solving such dexterous manipulation tasks often relies on imitation learning from demonstration datasets, which are typically collected via teleoperation systems and often demand substantial time and effort. To address these challenges, we present ViTaMIn, an embodiment-free manipulation interface that seamlessly integrates visual and tactile sensing into a hand-held gripper, enabling data collection without the need for teleoperation. Our design employs a compliant Fin Ray gripper with tactile sensing, allowing operators to perceive force feedback during manipulation for more intuitive operation. Additionally, we propose a multimodal representation learning strategy to obtain pre-trained tactile representations, improving data efficiency and policy robustness. Experiments on seven contact-rich manipulation tasks demonstrate that ViTaMIn significantly outperforms baseline methods, demonstrating its effectiveness for complex manipulation tasks.
Forward citations
Cited by 9 Pith papers
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Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design
A single diffusion transformer trains on tokenized robot bodies and motions to generate and optimize robot designs for unseen rewards and trajectories, outpacing evolutionary search in speed and often in reward.
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Feel the Force: Contact-Driven Learning from Humans
FeelTheForce trains a robot policy on human tactile demonstrations, predicting desired contact forces and using a PD controller to track them on the robot gripper, achieving 77% success across five force-sensitive tasks.
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S2A2: Audio-Visual Imitation Learning for Manipulation Tasks Using Acoustic Spatial Information
S2A2 adds microphone-array spatial audio and spectrograms to imitation-learning policies, substantially improving success on manipulation tasks where vision alone cannot identify the target or destination.
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$N_0$-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens
A VLA with predictive latent tactile tokens pretrained on large-scale visuo-tactile data, plus ALTER offline advantage labeling, leads contact-rich real and sim benchmarks.
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Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories
Pre-training a VLA model on 100k hours of auto-labeled UMI trajectories, then post-training on robot data, yields SOTA simulated manipulation and data-efficient fine-tuning.
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
A policy-agnostic two-stage real-world RL method learns tactile residual corrections on frozen visual policies, lifting contact-rich task success from 5–40% to 85–100% in under 80 minutes.
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ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation
An action-conditioned visuo-tactile world model generates synthetic camera-plus-touch rollouts that, mixed with real demonstrations, improve downstream contact-rich manipulation policies.
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Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
Agent spatial intelligence is organized into six neuroscience-inspired modules, and the field is reviewed through that lens without any experimental validation.
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Data Pyramid for Embodied Manipulation
Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.
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