Lie-Algebra Attention defines tokens as bare matrix Lie group elements and computes attention scores from the negative squared algebra norm of the relative pose log(g_i^{-1} g_j).
Actionflow: Equivariant, accurate, and efficient policies with spatially symmetric flow matching
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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A morphologically equivariant flow matching policy for bimanual robots enforces reflective symmetry to improve sample efficiency and enable zero-shot generalization to mirrored task configurations.
Chronos elevates full observation history to the policy's latent state via selective SSM tokens and a Schrödinger-inspired acceleration bridge, achieving large gains on memory-dependent robot tasks with fewer parameters.
IDP generates one-step robot actions by adaptively weighting a scalar potential objective using conditional expert geometry derived from local variations of observation-similar expert actions, combined with expert-proximal terminal evaluation.
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.
A multimodal visuotactile imitation learning framework using transformers and flow-based models improves robotic performance on the contact-rich task of match lighting.
citing papers explorer
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The Token Is a Group Element: On Lie-Algebra Attention over Matrix Lie Groups
Lie-Algebra Attention defines tokens as bare matrix Lie group elements and computes attention scores from the negative squared algebra norm of the relative pose log(g_i^{-1} g_j).
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Morphologically Equivariant Flow Matching for Bimanual Mobile Manipulation
A morphologically equivariant flow matching policy for bimanual robots enforces reflective symmetry to improve sample efficiency and enable zero-shot generalization to mirrored task configurations.
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Chronos: A Physics-Informed Full-History Framework for Non-Markovian Long-Horizon Manipulation
Chronos elevates full observation history to the policy's latent state via selective SSM tokens and a Schrödinger-inspired acceleration bridge, achieving large gains on memory-dependent robot tasks with fewer parameters.
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Implicit Drifting Policy: One-Step Action Generation via Conditional Expert Geometry
IDP generates one-step robot actions by adaptively weighting a scalar potential objective using conditional expert geometry derived from local variations of observation-similar expert actions, combined with expert-proximal terminal evaluation.
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Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
An RL data generation pipeline with generalizable rewards and language annotations produces diverse synthetic datasets that improve multi-task policy generalization on three bimanual manipulation tasks.
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General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling
GAM framework uses arc-length parameterization for temporal invariance and schema-affine factorization for geometric invariance to build a covariant action manifold integrated into VLA models for improved generalization from sparse data.
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On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting
A multimodal visuotactile imitation learning framework using transformers and flow-based models improves robotic performance on the contact-rich task of match lighting.