ArXiv

SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception

2026-07-29 · 14:41 UTC ·Gabe Everett, Brice Gunter, Ryan Vander Stelt... ·1 min read
Authors
Gabe Everett, Brice Gunter, Ryan Vander Stelt...
Categories
cs.RO, cs.AI, cs.LG
arXiv
https://arxiv.org/abs/2607.26985v1
PDF
https://arxiv.org/pdf/2607.26985v1

Brief

SymmGrid addresses slow wall-clock on-robot RL by applying parallelized symmetry transformations at the trajectory level: a symmetry tree generates many admissible state-action equivalences and homography-based visual warping aligns ego/exocentric views with proprioception. Evaluated on real peg-insertion, cable-routing, and object relocation tasks, it yields 1.37–2.17x faster convergence, 1.09–1.27x higher success, and up to 2.59x nAUC versus SOTA.

Why it matters

SymmGrid is a trajectory-level data-augmentation framework that models an MDP under a symmetry tree to apply parallelized invariant transformations (creating a geometric grid) to ego-/exocentric image + proprioception states; proprioceptive-conditioned visual warping uses homographies so augmented samples remain spatially consistent.

Key details

  • On real-robot manipulation tasks (peg-insertion, cable routing, object relocation) SymmGrid sped wall-clock training convergence by 1.37–2.17x, improved evaluation success rates by 1.09–1.27x, achieved normalized AUC improvements up to 2.59x, and reported fastest convergence times of 16.6, 10.9, and 79.3 minutes respectively.
  • All evaluations were performed directly on physical robots for contact-rich tasks, and authors claim SymmGrid’s branch symmetries super-scale replay diversity to move toward sub-10-minute on-robot learning for arms and humanoids (project: symmgrid-robot.github.io).
Source evidence

Abstract

Deep reinforcement policy learning directly in physical robots (on-robot learning) remains bottlenecked by slow wall-clock training times. We present SymmGrid, a trajectory level augmentation framework inspired by parallelized symmetries that super-scales group transformations to significantly accelerate on-robot learning in both egocentric and exocentric visual setups. We model a Markov Decision Process (MDP) under a symmetry tree, in which state-action pairs have admissible parallelized invariant transformations that yield a geometric grid structure. The state is modelled with ego- or exocentric images and proprioception information. The latter require special treatment, in the form of homographies, to warp visual scenes in line with their corresponding spatial transformations. These parallelized transformations produce a large set of unique symmetric equivalences that populate the replay buffer with diverse and consistent experiences that speed up learning and improve performance. We present extensive training and evaluations performed directly on real robot manipulation contact tasks including peg-insertions, cable routing, and object relocations. Relative to SOTA, SymmGrid achieved wall-clock training convergence speed-ups of 1.37-2.17x, evaluation success rate improvements of 1.09x-1.27x, fastest training convergence times of 16.6, 10.9, and 79.3 minutes respectively. For trajectory wide assessments, we used normalized area under the curve (nAUC) ratios. SymmGrid achieved improvements of up to 2.59x. These results confirm that simple branch symmetries can have an outsized result due to super-scaling and bring us closer to sub-10 minute on-robot learning training in manipulation tasks suitable for arms and humanoids. The project page is available at symmgrid-robot.github.io

Comment: 9 pages, 7 figures, 1 table