Equivariant Goal Conditioned Contrastive Reinforcement Learning

Self-supervised learning has emerged as a pivotal ingredient behind recent scale-driven breakthroughs, where large unlabeled datasets are used to learn powerful representations. However, in the context of reinforcement learning (RL), self-supervision plays a fundamentally different role. Rather than learning from a static dataset, self-supervised RL focuses on learning optimal control-policies through unlabeled sequential interactions with the environment--without relying on manual reward design or human annotation. Such a learning paradigm can enable scalable robot learning systems that autonomously acquire a broad repertoire of behaviors, generalize across tasks, and adapt to new environments with minimal human intervention [1, 2, 3, 4]. However, achieving this level of autonomy is challenging due to the inherent difficulties of exploration, sparse rewards, and the need for learning robust representations from high-dimensional sensory inputs. Goal-Conditioned Reinforcement Learning (GCRL) provides a natural framework for this paradigm, as it enables agents to learn to reach states sampled from a goal distribution and can be formulated without requiring externally provided rewards or expert supervision. Recent work [5] has explored the use of contrastive representation learning for GCRL-- an approach commonly referred to as Contrastive RL (CRL). This class of methods learns a goal-conditioned Q-function by aligning reparXiv:2507.16139v1