Toward Deployable Multi-Robot Collaboration via a Symbolically-Guided Decision Transformer

• URL: http://arxiv.org/abs/2508.13877v1
• Date: Tue, 19 Aug 2025 14:42:18 GMT
• Title: Toward Deployable Multi-Robot Collaboration via a Symbolically-Guided Decision Transformer
• Authors: Rathnam Vidushika Rasanji, Jin Wei-Kocsis, Jiansong Zhang, Dongming Gan, Ragu Athinarayanan, Paul Asunda,
• Abstract summary: Symbolically-Guided Decision Transformer (SGDT) integrates a neuro-symbolic mechanism with a causal transformer to enable deployable multi-robot collaboration.<n>We evaluate the performance of SGDT across a range of task scenarios, including zero-shot and few-shot scenarios.
• Score: 1.0242313198232116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning (RL) has demonstrated great potential in robotic operations. However, its data-intensive nature and reliance on the Markov Decision Process (MDP) assumption limit its practical deployment in real-world scenarios involving complex dynamics and long-term temporal dependencies, such as multi-robot manipulation. Decision Transformers (DTs) have emerged as a promising offline alternative by leveraging causal transformers for sequence modeling in RL tasks. However, their applications to multi-robot manipulations still remain underexplored. To address this gap, we propose a novel framework, Symbolically-Guided Decision Transformer (SGDT), which integrates a neuro-symbolic mechanism with a causal transformer to enable deployable multi-robot collaboration. In the proposed SGDT framework, a neuro-symbolic planner generates a high-level task-oriented plan composed of symbolic subgoals. Guided by these subgoals, a goal-conditioned decision transformer (GCDT) performs low-level sequential decision-making for multi-robot manipulation. This hierarchical architecture enables structured, interpretable, and generalizable decision making in complex multi-robot collaboration tasks. We evaluate the performance of SGDT across a range of task scenarios, including zero-shot and few-shot scenarios. To our knowledge, this is the first work to explore DT-based technology for multi-robot manipulation.

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