Versatile Offline Imitation from Observations and Examples via Regularized State-Occupancy Matching

Ma, Yecheng Jason; Shen, Andrew; Jayaraman, Dinesh; Bastani, Osbert

doi:10.48550/arxiv.2202.02433

Cited by 1 publication

(1 citation statement)

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“…Value Implicit Pre-Training (VIP). VIP (Ma et al, 2022b) learns the optimal goal-conditioned value function via the dual goal-conditioned RL formulation (Ma et al, 2022a;:…”

Section: Preliminaries and Problem Settingmentioning

confidence: 99%

Likelihood-Based Diverse Sampling for Trajectory Forecasting

Jason¹,

Inala

Jayaraman

et al. 2021

2021 IEEE/CVF International Conference on Computer Vision (ICCV)

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We present Language-Image Value learning (LIV), a unified objective for vision-language representation and reward learning from action-free videos with text annotations. Exploiting a novel connection between dual reinforcement learning and mutual information contrastive learning, the LIV objective trains a multi-modal representation that implicitly encodes a universal value function for tasks specified as language or image goals. We use LIV to pre-train the first control-centric vision-language representation from large human video datasets such as EpicKitchen. Given only a language or image goal, the pre-trained LIV model can assign dense rewards to each frame in videos of unseen robots or humans attempting that task in unseen environments. Further, when some target domain-specific data is available, the same objective can be used to fine-tune and improve LIV and even other pre-trained representations for robotic control and reward specification in that domain. In our experiments on several simulated and real-world robot environments, LIV models consistently outperform the best prior input state representations for imitation learning, as well as reward specification methods for policy synthesis. Our results validate the advantages of joint vision-language representation and reward learning within the unified, compact LIV framework. Project website: penn-pal-lab.github.io/LIV

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“…Value Implicit Pre-Training (VIP). VIP (Ma et al, 2022b) learns the optimal goal-conditioned value function via the dual goal-conditioned RL formulation (Ma et al, 2022a;:…”

Section: Preliminaries and Problem Settingmentioning

confidence: 99%