Pith. sign in

REVIEW 10 cited by

See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.03858 v2 pith:4P76NSJQ submitted 2022-12-07 cs.RO cs.CV

See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation

classification cs.RO cs.CV
keywords manipulationroboticmodalitiestasksfeelhearperceptionrobot
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

Humans use all of their senses to accomplish different tasks in everyday activities. In contrast, existing work on robotic manipulation mostly relies on one, or occasionally two modalities, such as vision and touch. In this work, we systematically study how visual, auditory, and tactile perception can jointly help robots to solve complex manipulation tasks. We build a robot system that can see with a camera, hear with a contact microphone, and feel with a vision-based tactile sensor, with all three sensory modalities fused with a self-attention model. Results on two challenging tasks, dense packing and pouring, demonstrate the necessity and power of multisensory perception for robotic manipulation: vision displays the global status of the robot but can often suffer from occlusion, audio provides immediate feedback of key moments that are even invisible, and touch offers precise local geometry for decision making. Leveraging all three modalities, our robotic system significantly outperforms prior methods.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. M2R2: MultiModal Robotic Representation for Temporal Action Segmentation

    cs.RO 2025-04 unverdicted novelty 7.0

    M2R2 proposes a multimodal robotic representation for temporal action segmentation that combines proprioceptive and exteroceptive sensors with a novel training strategy enabling feature reuse across models, achieving ...

  2. Kepler-Encoder-v0.1: Towards a Multimodal Embedding Model for Robots

    cs.RO 2026-07 conditional novelty 6.0

    A self-supervised multimodal encoder trained with vision, proprioception, and force yields a vision-only latent that recovers end-effector state and force above vision baselines on RH20T, with modest absolute force accuracy.

  3. OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies

    cs.RO 2026-07 conditional novelty 6.0

    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.

  4. UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models

    cs.RO 2026-06 unverdicted novelty 6.0

    UniTacVLA builds a state-aware and dynamics-aware tactile prior via unified latent space, tactile chain-of-thought, and mixed real/predicted feedback controller to boost dexterous manipulation performance.

  5. Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation

    cs.RO 2026-06 unverdicted novelty 6.0

    MiTaS fuses multi-resolution tactile data from GelSight and Evetac sensors with vision using modality-specific stems and transformer fusion to condition flow-matching policies, reporting 80% average success on five co...

  6. Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation

    cs.RO 2025-12 unverdicted novelty 6.0

    DreamTacVLA grounds VLA models in contact physics by aligning multi-scale vision-tactile inputs and predicting future tactile states, reaching up to 95% success on contact-rich tasks.

  7. Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation

    cs.RO 2025-12 conditional novelty 6.0

    A vision-language-action policy that predicts future tactile images and uses that predicted touch to refine its actions reaches up to 95% success on contact-rich manipulation.

  8. Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

    cs.RO 2025-11 unverdicted novelty 6.0

    MSDP pretrains a transformer encoder via masked multisensory reconstruction and feeds the embeddings into an asymmetric actor-critic RL setup, yielding faster learning and high real-robot success rates with only 6,000...

  9. Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation

    cs.RO 2026-06 unverdicted novelty 5.0

    A visuo-tactile policy learning method that exploits tactile motion correlation for contact state distinction and Mixture-of-Transformers for cross-modal fusion.

  10. Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

    cs.RO 2025-11 conditional novelty 5.0

    MSDP pre-trains a transformer encoder with masked multisensory autoencoding, then uses an asymmetric actor-critic bridge (cross-attention for critic, pooling for actor) to accelerate and robustify contact-rich RL acro...