REVIEW 5 cited by
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data
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
abstract
Robotic systems that rely primarily on self-supervised learning have the potential to decrease the amount of human annotation and engineering effort required to learn control strategies. In the same way that prior robotic systems have leveraged self-supervised techniques from computer vision (CV) and natural language processing (NLP), our work builds on prior work showing that the reinforcement learning (RL) itself can be cast as a self-supervised problem: learning to reach any goal without human-specified rewards or labels. Despite the seeming appeal, little (if any) prior work has demonstrated how self-supervised RL methods can be practically deployed on robotic systems. By first studying a challenging simulated version of this task, we discover design decisions about architectures and hyperparameters that increase the success rate by $2 \times$. These findings lay the groundwork for our main result: we demonstrate that a self-supervised RL algorithm based on contrastive learning can solve real-world, image-based robotic manipulation tasks, with tasks being specified by a single goal image provided after training.
Forward citations
Cited by 5 Pith papers
-
Good Rankers, Bad Objectives: Bilinear Contrastive Critics under Expressive Policy Search
Bilinear contrastive critics remain good compatibility rankers but are unsafe to maximize for action selection; cosine bounding does not fix value decalibration, while Bellman TD-Q does.
-
ACDC: Adaptive Curriculum Planning with Dynamic Contrastive Control for Goal-Conditioned Reinforcement Learning in Robotic Manipulation
ACDC uses adaptive curriculum planning and norm-constrained contrastive learning to improve sample efficiency and success rates over baselines in robotic goal-conditioned RL tasks.
-
Can We Really Learn One Representation to Optimize All Rewards?
Finite-dimensional FB representations cannot exactly encode all rewards in continuous control; a new one-step FB variant that fits the behavioral policy converges better and beats FB on average.
-
Equivariant Goal Conditioned Contrastive Reinforcement Learning
Equivariant Contrastive RL imposes C8 rotation symmetry on the critic and actor, improving sample efficiency and goal generalization in simulated manipulation.
-
Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization
GCReinSL adds Q-conditioned maximization to supervised offline RL, using normalizing flows to estimate goal-reaching probabilities and expectile regression to condition actions on the best in-distribution value, impro...
Discussion (0). Sign in to comment.