VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation

• URL: http://arxiv.org/abs/2606.21600v1
• Date: Fri, 19 Jun 2026 16:56:54 GMT
• Title: VQActFlow: Vector-Quantized Action Mode Steering for Multi-Task Robot Manipulation
• Abstract summary: VQActFlow is a multi-task manipulation policy that tokenizes action chunks and generates code sequences.<n>Inference-time guidance acts on this preference to steer mode commitment.<n>We evaluate VQActFlow on three platforms: the LIBERO simulation benchmarks, a Unitree G1 humanoid performing whole-body pick-and-place, and an ALOHA-style bimanual platform performing contact-rich tasks.
• Score: 11.975174558402864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task robot manipulation policies are challenging to learn from demonstration because traditionally a single network must select among qualitatively different action modes from a multimodal demonstration distribution, conditioned on language and visual context. A wrong mode selection means executing the wrong task or an action infeasible in the scene. Tokenizing continuous actions into a learned discrete codebook separates these modes at the representation level, offering structural advantages for multi-task learning. We propose VQActFlow, a multi-task manipulation policy that tokenizes action chunks and generates code sequences via Variational Flow Matching. VQActFlow maintains an explicit preference over action modes throughout generation. Inference-time guidance acts on this preference to steer mode commitment. We instantiate this with classifier-free guidance over language conditioning, which steers the policy toward the instructed action mode, and a learned codebook critic that supplies a complementary feasibility signal. We evaluate VQActFlow on three platforms: the LIBERO simulation benchmarks, a Unitree G1 humanoid performing whole-body pick-and-place, and an ALOHA-style bimanual platform performing contact-rich tasks. Across these benchmarks, VQActFlow outperforms both continuous and discrete baselines.

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