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SymmGrid Accelerates Robot Learning

SymmGrid framework dramatically accelerates on-robot learning for manipulation tasks, achieving up to 2.17x speed-ups and moving closer to sub-10 minute training.

6 min read
Diagram illustrating the SymmGrid framework's symmetry transformations.
SymmGrid utilizes parallelized symmetries to accelerate robot learning.

Visual TL;DR. Slow Robot Learning addresses SymmGrid Framework. SymmGrid Framework uses Super-Scales Transformations. Super-Scales Transformations creates Geometric Grid Structure. Geometric Grid Structure provides Diverse Experiences. Diverse Experiences leads to Faster Learning. Faster Learning achieves 2.17x Speed-ups. 2.17x Speed-ups seen in Real-World Gains.

  1. Slow Robot Learning: agonizingly slow wall-clock training times bottleneck deep reinforcement policy learning for physical robots
  2. SymmGrid Framework: novel trajectory-level augmentation framework inspired by parallelized symmetries for on-robot learning
  3. Super-Scales Transformations: models Markov Decision Process under a symmetry tree, endowing state-action pairs with invariant transformations
  4. Geometric Grid Structure: created by parallelized invariant transformations, populating the replay buffer more effectively
  5. Diverse Experiences: grid structure populated with diverse and consistent experiences from egocentric and exocentric visual setups
  6. Faster Learning: diversity and consistency of experiences directly translate to faster learning and improved performance
  7. 2.17x Speed-ups: dramatically accelerates on-robot learning for manipulation tasks, moving closer to sub-10 minute training
  8. Real-World Gains: efficacy demonstrated on tangible gains on real-world manipulation tasks for physical robots
Visual TL;DR
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. Faster Learning achieves 2.17x Speed-ups addresses achieves Slow Robot Learning SymmGrid Framework Faster Learning 2.17x Speed-ups From startuphub.ai · The publishers behind this format
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. Faster Learning achieves 2.17x Speed-ups addresses achieves Slow RobotLearning SymmGridFramework Faster Learning 2.17x Speed-ups From startuphub.ai · The publishers behind this format
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. Faster Learning achieves 2.17x Speed-ups addresses achieves Slow Robot Learning agonizingly slow wall-clock training timesbottleneck deep reinforcement policylearning for physical robots SymmGrid Framework novel trajectory-level augmentationframework inspired by parallelizedsymmetries for on-robot learning Faster Learning diversity and consistency of experiencesdirectly translate to faster learning andimproved performance 2.17x Speed-ups dramatically accelerates on-robot learningfor manipulation tasks, moving closer tosub-10 minute training From startuphub.ai · The publishers behind this format
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. Faster Learning achieves 2.17x Speed-ups addresses achieves Slow RobotLearning agonizingly slowwall-clock trainingtimes bottleneck… SymmGridFramework noveltrajectory-levelaugmentation… Faster Learning diversity andconsistency ofexperiences… 2.17x Speed-ups dramaticallyaccelerateson-robot learning… From startuphub.ai · The publishers behind this format
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. SymmGrid Framework uses Super-Scales Transformations. Super-Scales Transformations creates Geometric Grid Structure. Geometric Grid Structure provides Diverse Experiences. Diverse Experiences leads to Faster Learning. Faster Learning achieves 2.17x Speed-ups. 2.17x Speed-ups seen in Real-World Gains addresses uses creates provides leads to achieves seen in Slow Robot Learning agonizingly slow wall-clock training timesbottleneck deep reinforcement policylearning for physical robots SymmGrid Framework novel trajectory-level augmentationframework inspired by parallelizedsymmetries for on-robot learning Super-Scales Transformations models Markov Decision Process under asymmetry tree, endowing state-action pairswith invariant transformations Geometric Grid Structure created by parallelized invarianttransformations, populating the replaybuffer more effectively Diverse Experiences grid structure populated with diverse andconsistent experiences from egocentric andexocentric visual setups Faster Learning diversity and consistency of experiencesdirectly translate to faster learning andimproved performance 2.17x Speed-ups dramatically accelerates on-robot learningfor manipulation tasks, moving closer tosub-10 minute training Real-World Gains efficacy demonstrated on tangible gains onreal-world manipulation tasks for physicalrobots From startuphub.ai · The publishers behind this format
Visual TL;DR, startuphub.ai Slow Robot Learning addresses SymmGrid Framework. SymmGrid Framework uses Super-Scales Transformations. Super-Scales Transformations creates Geometric Grid Structure. Geometric Grid Structure provides Diverse Experiences. Diverse Experiences leads to Faster Learning. Faster Learning achieves 2.17x Speed-ups. 2.17x Speed-ups seen in Real-World Gains addresses uses creates provides leads to achieves seen in Slow RobotLearning agonizingly slowwall-clock trainingtimes bottleneck… SymmGridFramework noveltrajectory-levelaugmentation… Super-ScalesTransformations models MarkovDecision Processunder a symmetry… Geometric GridStructure created byparallelizedinvariant… DiverseExperiences grid structurepopulated withdiverse and… Faster Learning diversity andconsistency ofexperiences… 2.17x Speed-ups dramaticallyaccelerateson-robot learning… Real-World Gains efficacydemonstrated ontangible gains on… From startuphub.ai · The publishers behind this format

The bottleneck in deep reinforcement policy learning for physical robots has long been agonizingly slow wall-clock training times. This paper introduces a novel approach that significantly accelerates this process.

Super-Scaling Group Transformations for Faster Convergence

The researchers present SymmGrid, a trajectory-level augmentation framework. Inspired by parallelized symmetries, it super-scales group transformations to dramatically speed up on-robot learning. The core idea models a Markov Decision Process under a symmetry tree. State-action pairs are endowed with parallelized invariant transformations, creating a geometric grid structure. This structure, populated with diverse and consistent experiences from both egocentric and exocentric visual setups, populates the replay buffer more effectively. This diversity and consistency directly translate to faster learning and improved performance.

Tangible Gains on Real-World Manipulation Tasks

SymmGrid's efficacy was demonstrated on real robot manipulation contact tasks, including peg insertions, cable routing, and object relocations. Compared to state-of-the-art methods, SymmGrid achieved wall-clock training convergence speed-ups ranging from 1.37x to 2.17x. Success rate improvements were observed between 1.09x and 1.27x. Notably, SymmGrid achieved the fastest training convergence times at 16.6 minutes for peg insertions, 10.9 minutes for cable routing, and 79.3 minutes for object relocations. Trajectory-wide assessments using normalized area under the curve (nAUC) ratios showed improvements of up to 2.59x. These results strongly suggest that even simple branch symmetries can yield outsized results through super-scaling, a critical step towards achieving sub-10 minute on-robot learning for manipulation tasks suitable for robotic arms and humanoids.

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