TAKO demonstrates real-time adversarial takeover of robotic diffusion policies via reusable universal patches on visual inputs, achieving 100% success in steering attacker-chosen trajectories across multiple tasks, encoders, and diffusion methods.
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Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
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abstract
This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is publicly available diffusion-policy.cs.columbia.edu
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- abstract This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynam
- extension ceive, modulate, and record internal grasping force, external interaction wrench, and tactile interaction during demon- stration. In this sense, OmniUMI does not merely measure physical variables; it organizes their acquisition around a human-aligned interface for natural interaction regulation. At the learning and deployment level, we extend dif- fusion policy (DP) [6] to incorporate multimodal observa- tions, including visual, tactile, and force-related signals, en- abling policies to reason j
- baseline 9 53.9 42.0 32.0 2.89 TaKSIE [13] WACV, 2025 Plan 1 90.4 73.9 61.7 51.2 40.8 3.18 HULC++ [26] ICRA, 2023 Plan 1 93.0 79.0 64.0 52.0 40.0 3.30 RoboTron-Mani [40] ICCV, 2025 3D input 1 94.7 80.3 65.1 51.4 39.0 3.31 DaDu-Corki-SW [9] ISCA, 2025 - 1 92.3 80.0 67.4 56.6 45.8 3.42 RoboUniView (default) [23] arXiv, 2024 - 195.4 82.768.5 56.4 46.1 3.49 DTP [7] RA-L, 2025 Plan 1 92.4 81.970.2 60.3 50.9 3.55 NFE = 4 methods LaDi-WM [10] arXiv, 2025 WM≈4 92.7 83.1 72.1 61.2 54.1 3.63 LightDP [38] ICCV, 202
- dataset grounding [9, 290], world modeling [291, 292], and temporal prediction [293]. Since web data primarily consistsofvision-languagepairsandismainlyusedtoenhancefoundationmodelcapabilities, whichfallsoutside the scope of our survey, we focus primarily on human video in our discussion. Representative human video datasets include Ego4D [236], EPIC-KITCHENS-100 [277], and Something-Something V2 [275]. Although these datasets do not contain action labels directly usable for policy learning, they capture
- method network with data annotated at the sub-step level. Consequently, our method can complete com- plex tasks, like laundry folding, without requiring an external high-level policy, making the entire framework more end-to-end and demonstrating significant potential. Diffusion models. Diffusion models [48, 49, 50] have emerged as the dominant approach in visual generation. The Diffusion Policy [30] successfully applies the diffusion model to robot learning, demonstrating its ability to model multimoda
- method [2] primarily relied on autoregressive or behavior-cloning policies that predict actions step by step. More recently, dual-system VLA architectures [1, 9] have emerged, decoupling high-level multimodal reasoning [19, 22] from low-level action generation. In these systems, a perception-language back- bone [19] produces contextual representations, while a dedicated action expert is often implemented as a diffusion [23] or flow-matching head that predicts multiple future actions in parallel [24, 25
- background models in current usage; both are most usefully read here as design boundaries rather than additional roles. 10.1 Generative world simulators Generative world simulators form a boundary case. Systems inspired by large-scale video generation, interactive environment generation, and physical-AI simulation platforms are often called world models because they produce plausible future observations or playable environments [12, 49, 64], but photorealistic or interactive generation is not identical to
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representative citing papers
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
REGEN uses recurrent generative replays from World Action Models to cut catastrophic forgetting by up to 50% in continual imitation learning compared to sequential fine-tuning.
A co-embodied wearable robotic hand uses variable autonomy with a visuomotor diffusion policy for grasping and head gestures for actuation, yielding 23.3% faster task times and 93.6% success in a 44-person study.
ECD reformulates compositional diffusion planning as energy minimization over local bridge potentials, adding a boundary reaction term and a Markov score approximation that runs in linear time.
NAC adapts multi-scale RVQGAN audio codecs with kinematic-specific losses to produce ordered action tokens that yield lower reconstruction error and higher task success than prior tokenizers in VLA models.
Flow Reversal Steering steers flow matching generalist policies by reversing suboptimal actions to nearby better modes, enabling improved zero-shot control, quick distillation, and RL bootstrapping in robotic manipulation.
Ambient Diffusion Policy enables better imitation learning from suboptimal robot data by leveraging spectral properties to restrict data usage to specific diffusion times.
ActProbe is an action-space detector that uses temporal consistency error and action chunk magnitude from policy outputs, mapped via LSTM-MLP, to predict failures earlier than baselines across policies and real-robot tasks.
DVAC uses denoising variance as an intrinsic signal to adaptively chunk actions in flow-based robot policies, improving success rates and cutting replans on LIBERO, RoboTwin, CALVIN, and real-world tasks.
BOKBO is the first conformal abstention method for K-sample VLA policies that supplies finite-sample distribution-free guarantees on executed violation rates, with global and Mondrian per-task variants.
CGPO integrates training-free critic guidance into diffusion denoising to produce high-Q actions as regression targets, yielding SOTA results on MuJoCo locomotion and successful Franka arm grasping.
VLA architectures exhibit architecture-specific failure signatures at the motor-command level, with direction reversal as a universal predictor and velocity monitoring ineffective for continuous models.
Soft RTC uses partially denoised states for overlap tokens and token-wise blending to reduce action delta and jerk by ~9% versus hard RTC while matching solve rates on Kinetix levels.
JOPAT jointly models pixels, point tracks, and actions in a diffusion transformer and reports gains over pixel-only baselines on long-horizon robot tasks with occlusion and off-screen motion.
SkiP introduces action relabeling and Motion Spectrum Keying to skip redundant steps in robot trajectories, cutting executed steps by 15-40% while maintaining success rates across 72 simulated and 3 real tasks.
DSSP is a history-conditioned diffusion state space policy that uses SSMs to encode full observation streams with an auxiliary dynamics objective and hierarchical fusion, achieving SOTA results with reduced model size in robot manipulation.
OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.
World models succeed when their latent states are built to meet task-specific sufficiency constraints rather than preserving the maximum amount of information.
Action Agent pairs LLM-driven video generation with a flow-constrained diffusion transformer to produce velocity commands, raising video success to 86% and delivering 64.7% real-world navigation on a Unitree G1 humanoid.
A cross-version swap protocol reveals dominant skills that swing composition success by up to 50 percentage points, and an atomic probe with selective revalidation governs updates at lower cost than always re-testing full compositions.
CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
VLA models exhibit a compute-bound VLM phase followed by a memory-bound action phase on edge hardware; DP-Cache and V-AEFusion reduce redundancy and enable pipeline parallelism for up to 6x speedup on NPUs with marginal task degradation.
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
citing papers explorer
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Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Mask World Model predicts semantic mask dynamics with video diffusion and integrates it with a diffusion policy head, outperforming RGB world models on LIBERO and RLBench while showing better real-world generalization and texture robustness.
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Referring-Aware Visuomotor Policy Learning for Closed-Loop Manipulation
ReV is a referring-aware visuomotor policy using coupled diffusion heads for real-time trajectory replanning in robotic manipulation, trained solely via targeted perturbations to expert demonstrations and achieving higher success rates in simulated and real tasks.
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Dynamic Execution Commitment of Vision-Language-Action Models
A3 adaptively sets a VLA model's execution horizon to the longest action prefix the model re-confirms under conditional and sequential re-decoding, matching or improving task success without manual tuning.
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DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
DexVLA combines a scaled diffusion action expert with embodiment curriculum learning to achieve better generalization and performance than prior VLA models on diverse robot hardware and long-horizon tasks.
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RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
RoboCasa supplies a large-scale kitchen simulator, generative assets, 100 tasks, and automated data pipelines that produce a clear scaling trend in imitation learning for generalist robots.
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3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
3D Diffuser Actor unifies diffusion policies with 3D scene features to set new state-of-the-art results on RLBench and CALVIN robot benchmarks.
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Training Diffusion Models with Reinforcement Learning
DDPO uses policy gradients on the denoising process to optimize diffusion models for arbitrary rewards like human feedback or compressibility.