Unified Condition-Action Modeling for Accurate One-Step Action Generation

• URL: http://arxiv.org/abs/2608.16153v3
• Date: Sun, 23 Aug 2026 16:38:01 GMT
• Title: Unified Condition-Action Modeling for Accurate One-Step Action Generation
• Abstract summary: UCA-Flow is a unified condition-action modeling framework for accurate one-step action generation.<n>Our method unifies observation conditions, timestep conditions, interval conditions, and action tokens into a single sequence, and processes them with a Unified Condition-Action Transformer.
• Score: 24.554218523494498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robot manipulation requires policies that are both accurate and efficient, as robot control must respond to changing observations under tight latency constraints. Recent diffusion and flow policies are promising, but they often treat conditions as auxiliary signals rather than jointly evolving them with action trajectories. We find that this limitation can be effectively mitigated by a \textbf{simple yet effective unified condition-action modeling design} that represents conditions and actions in a shared token space, allowing a compact model to achieve high performance while improving both inference speed and accuracy. Therefore, we propose UCA-Flow, a unified condition-action modeling framework for accurate one-step action generation. Our method unifies observation conditions, timestep conditions, interval conditions, and action tokens into a single sequence, and processes them with a Unified Condition-Action Transformer for joint condition-action representation learning. As a result, condition representations are dynamically reconstructed according to the current generation stage, highlighting information most relevant for action refinement. Furthermore, we introduce an improved dual-pass supervision scheme over $u$ and $v$ for stronger optimization of unified condition-action modeling. UCA-Flow improves the average success rate by 9.3 percentage points over the strongest baseline, while achieving $45.6\times$ and $33.4\times$ speedups over DP3 and Simple DP3, and remaining $4.3\times$ and $2.3\times$ faster than one-step FlowPolicy and MP1, respectively.

Related papers

• FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking [51.97839311685636]
  FutureRTC is a plug-and-play adaptation framework that predicts execution-time observations and states for asynchronous VLA control without modifying the underlying policy.<n>We show that FutureRTC achieves robustness to inference delays, resulting in smoother trajectories, faster execution, and consistently higher task success rates.
  arXiv  Detail & Related papers  (2026-07-27T05:10:16Z)
• PIER-Flow: Physics-Informed Efficient Rectified Flow for Real-Time Mobile Robot Navigation [10.164953732323463]
  PIER-Flow is a lightweight navigation policy for mobile robots.<n>It achieves a 98.85% success rate and zero collisions, with an average inference of $sim$1.29 ms.
  arXiv  Detail & Related papers  (2026-07-11T12:34:28Z)
• SA-VLA: State-aware tokenizer for improving Vision-Language-Action Models' performance [12.586771228417112]
  We propose SA-VLA, a state-aware action tokenizer that conditions action decoding on robot state.<n>On 12 RoboTwin manipulation tasks, SA-VLA improves the average success rate from 0.29 to 0.56 over the strongest tokenizer baseline.
  arXiv  Detail & Related papers  (2026-06-29T10:45:53Z)
• PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models [50.232333672172395]
  Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execution efficiency.<n>We propose textbfPolicyTrim, a reinforcement learning-based post-training framework that extends the reliable action chunk length and reduces redundant physical steps.<n>Our framework delivers up to a 5.83$times$ end-to-end deployment speedup without compromising task success rates.
  arXiv  Detail & Related papers  (2026-06-21T14:54:07Z)
• SC3-Eval: Evaluating Robot Foundation Models via Self-Consistent Video Generation [73.2142090645987]
  SC3-Eval is a self-consistent video generation recipe that adapts a pre-trained video foundation model into an accurate policy evaluator.<n>We demonstrate SC3-Eval rollouts reproduce the failure modes that policies exhibit in real-world rollouts, supporting fine-grained diagnostic comparison.
  arXiv  Detail & Related papers  (2026-06-17T02:15:46Z)
• Dynamic Execution Commitment of Vision-Language-Action Models [21.647844049489535]
  We introduce A3, an Adaptive Action Acceptance mechanism that reframes dynamic execution commitment as a self-speculative prefix verification problem.<n>A3 first computes a trajectory-wise consensus score of actions via group sampling, then selects a representative draft and prioritizes downstream verification.<n> Experiments across diverse VLA models and benchmarks demonstrate that A3 eliminates the need for manual horizon tuning while achieving a superior trade-off between execution and inference throughput.
  arXiv  Detail & Related papers  (2026-05-12T05:52:58Z)
• World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry [82.93104394404781]
  General-purpose world models promise scalable policy evaluation, optimization, and planning.<n>We propose World Action Verifier (WAV), a framework that enables world models to identify their own prediction errors and self-improve.
  arXiv  Detail & Related papers  (2026-04-02T12:48:36Z)
• Efficient Hybrid SE(3)-Equivariant Visuomotor Flow Policy via Spherical Harmonics for Robot Manipulation [51.48086451600577]
  E3Flow is a novel framework that addresses the limitations of equivariant diffusion policies.<n>It unifies efficient rectified flow with stable, multi-modal equivariant learning for the first time.<n>E3Flow achieves a 3.12% improvement in average success rate over the state-of-the-art Spherical Diffusion Policy.
  arXiv  Detail & Related papers  (2026-03-24T14:00:36Z)
• Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation [65.13627721310613]
  Mean velocity policy (MVP) is a new generative policy function that models the mean velocity field to achieve the fastest one-step action generation.<n>MVP achieves state-of-the-art success rates across several challenging robotic manipulation tasks from Robomimic and OGBench.
  arXiv  Detail & Related papers  (2026-02-14T14:44:06Z)
• CoLA-Flow Policy: Temporally Coherent Imitation Learning via Continuous Latent Action Flow Matching for Robotic Manipulation [4.726851899243877]
  LG-Flow Policy is a trajectory-level imitation learning framework that performs flow matching in a continuous latent action space.<n>By encoding action sequences into temporally regularized latent trajectories and learning an explicit latent-space flow, the proposed approach decouples global motion structure from low-level control noise.
  arXiv  Detail & Related papers  (2026-01-30T15:36:43Z)
• ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows [7.360373380580255]
  We introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, providing fast, geodesic-consistent, least-computational policy generation.<n>We find that the proposed ReSeFlow with only one inference step can achieve better performance with lower geodesic distance than the baseline methods.
  arXiv  Detail & Related papers  (2025-09-20T06:32:36Z)
• SP-VLA: A Joint Model Scheduling and Token Pruning Approach for VLA Model Acceleration [70.72227437717467]
  Vision-Language-Action (VLA) models have attracted increasing attention for their strong control capabilities.<n>Their high computational cost and low execution frequency hinder their suitability for real-time tasks such as robotic manipulation and autonomous navigation.<n>We propose SP-VLA, a unified framework that accelerates VLA models by jointly scheduling models and pruning tokens.
  arXiv  Detail & Related papers  (2025-06-15T05:04:17Z)
• OminiControl2: Efficient Conditioning for Diffusion Transformers [68.3243031301164]
  We present OminiControl2, an efficient framework that achieves efficient image-conditional image generation.<n>OminiControl2 introduces two key innovations: (1) a dynamic compression strategy that streamlines conditional inputs by preserving only the most semantically relevant tokens during generation, and (2) a conditional feature reuse mechanism that computes condition token features only once and reuses them across denoising steps.
  arXiv  Detail & Related papers  (2025-03-11T10:50:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.