PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models

Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for multimodal synthetic feedback and trajectory synthesis. Unlike prior approaches that rely on single-modality FM evaluations, PRIMT employs a hierarchical neuro-symbolic fusion strategy, integrating the complementary strengths of large language models and vision-language models in evaluating robot behaviors for more reliable and comprehensive feedback. PRIMT also incorporates foresight trajectory generation, which reduces early-stage query ambiguity by warm-starting the trajectory buffer with bootstrapped samples, and hindsight trajectory augmentation, which enables counterfactual reasoning with a causal auxiliary loss to improve credit assignment. We evaluate PRIMT on 2 locomotion and 6 manipulation tasks on various benchmarks, demonstrating superior performance over FM-based and scripted baselines.

Overview of the PRIMT, comprising of two synergistic modules: 1) Hierarchical neuro-symbolic preference fusion improves the quality and reliability of synthetic feedback by leveraging the complementary collective intelligence of VLMs and LLMs for multimodal evaluation of robot behaviors; and 2) Bidirectional trajectory synthesis consists of foresight trajectory generation, which bootstraps the trajectory buffer to mitigate early-stage query ambiguity, and hindsight trajectory augmentation, which applies SCM-based counterfactual reasoning to improve reward learning with a causal auxiliary loss that enables fine-grained credit assignment.