ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent
  Planning
Vision-language-action (VLA) reasoning tasks require agents to interpret
multimodal instructions, perform long-horizon planning, and act adaptively in
dynamic environments. Existing approaches typically train VLA models in an
end-to-end fashion, directly mapping inputs to actions without explicit
reasoning, which hinders their ability to plan over multiple steps or adapt to
complex task variations. In this paper, we propose ThinkAct, a dual-system
framework that bridges high-level reasoning with low-level action execution via
reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate
embodied reasoning plans guided by reinforcing action-aligned visual rewards
based on goal completion and trajectory consistency. These reasoning plans are
compressed into a visual plan latent that conditions a downstream action model
for robust action execution on target environments. Extensive experiments on
embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct
enables few-shot adaptation, long-horizon planning, and self-correction
behaviors in complex embodied AI tasks.
688044e800f4b5a05f2fbdd8
Chi-Pin Huang
claimed_verified
2025-07-23T08:36:38.485Z
64705d224be5cf1f3348d6bc
/avatars/270bff7c7cb326528dc192fc38561a8b.svg
Chi-Pin Huang
user
jasper0314-huang
688044e800f4b5a05f2fbdd9
Yueh-Hua Wu
688044e800f4b5a05f2fbdda
Min-Hung Chen
claimed_verified
2025-07-23T08:36:36.038Z
64ae22dd1aee69ece065cdcd
https://cdn-avatars.huggingface.co/v1/production/uploads/64ae22dd1aee69ece065cdcd/JG7QaHIrr4i2k4uwR4pZK.png
Min-Hung Chen
user
cmhungsteve
688044e800f4b5a05f2fbddb
Yu-Chiang Frank Wang
688044e800f4b5a05f2fbddc
Fu-En Yang
claimed_verified
2025-07-28T08:46:24.433Z
6312cab05beb528b5c1500e3
/avatars/a328e8cc99fb031b2d5c911c4b577e7e.svg
Fu-En Yang
user
FuEnYang
ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent
  Planning
Vision-language-action (VLA) reasoning tasks require agents to interpret
multimodal instructions, perform long-horizon planning, and act adaptively in
dynamic environments. Existing approaches typically train VLA models in an
end-to-end fashion, directly mapping inputs to actions without explicit
reasoning, which hinders their ability to plan over multiple steps or adapt to
complex task variations. In this paper, we propose ThinkAct, a dual-system
framework that bridges high-level reasoning with low-level action execution via
reinforced visual latent planning. ThinkAct trains a multimodal LLM to generate
embodied reasoning plans guided by reinforcing action-aligned visual rewards
based on goal completion and trajectory consistency. These reasoning plans are
compressed into a visual plan latent that conditions a downstream action model
for robust action execution on target environments. Extensive experiments on
embodied reasoning and robot manipulation benchmarks demonstrate that ThinkAct
enables few-shot adaptation, long-horizon planning, and self-correction
behaviors in complex embodied AI tasks.
ThinkAct, a dual-system framework, uses reinforced visual latent planning to enable few-shot adaptation, long-horizon planning, and self-correction in embodied AI tasks by bridging high-level reasoning with low-level action execution.
multimodal LLM embodied reasoning plans action-aligned visual rewards goal completion trajectory consistency visual plan latent action model few-shot adaptation long-horizon planning self-correction