Masked Generative Policy for Robotic Control
Authors: Lipeng Zhuang
Shiyu Fan
Florent P. Audonnet
Yingdong Ru
Edmond S. L. Ho
Gerardo Aragon-Camarasa
Paul Henderson
Venue: ICLR 2026 Poster (2026)
Keywords: Imitation Learning
Masked Generative Transformer
Generative Model
Primary area: applications to robotics, autonomy, planning

Abstract: We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer that generates tokens in parallel and then rapidly refines only low-confidence tokens. We further propose two new sampling paradigms: MGP-Short, which performs parallel masked generation with score-based refinement for Markovian tasks, and MGP-Long, which predicts full trajectories in a single pass and dynamically refines low-confidence action tokens based on new observations. With globally coherent prediction and robust adaptive execution capabilities, MGP-Long enables reliable control on complex and non-Markovian tasks that prior methods struggle with. Extensive evaluations on 150 robotic manipulation tasks spanning the Meta-World and LIBERO benchmarks show that MGP achieves both rapid inference and superior success rates compared to state-of-the-art diffusion and autoregressive policies. Specifically, MGP increases the average success rate by 9\% across 150 tasks while cutting per-sequence inference time by up to 35×. It further improves the average success rate by 60\% in dynamic and missing-observation environments, and solves two non-Markovian scenarios where other state-of-the-art methods fail.

Published as a conference paper at ICLR 2026
MASKED GENERATIVE POLICY FOR ROBOTIC CON- TROL
Lipeng Zhuang; Shiyu Fan;
Florent P. Audonnet, Yingdong Ru
Edmond S. L. Ho Gerardo Aragon Camarasa,
Paul Henderson
Department of Computer Science, University of Glasgow Glasgow, United Kingdom
ABSTRACT
We present Masked Generative Policy (MGP), novel framework for visuomotor imitation learning. We represent actions as discrete tokens, and train a conditional masked transformer that generates tokens in parallel and then rapidly refines only low-confidence tokens. We further propose two new sampling paradigms: MGP- Short, which performs parallel masked generation with score-based refinement for Markovian tasks, and MGP-Long, which predicts full trajectories a single pass and dynamically refines low-confidence action tokens based on new obser- vations. With globally coherent prediction and robust adaptive execution capabil- ities, MGP-Long enables reliable control on complex and Marakoooo tasks that prior methods struggle with. Extensive evaluations 150 robotic manipu- lation tasks spanning the Meta World and LIBERO benchmarks show that MGP achieves both rapid inference and superior success rates compared to state-of-the art diffusion and autoregressive policies. Specifically, MGP increases the average success rate by 9% across 150 tasks while cutting per-sequence inference time by up to 35x, further improves the average success rate by 60% in dynamic and
environments, and solves two non-Markovian scenarios where other state-of-te toffthh-an art methods fail.
INTRODUCTION
Enabling robots to perform complex manipulation tasks directly from high-dimensional sensory in- puts, such as vision, remains a challenge in robotics and artificial intelligence. Imitation learning has emerged promising and data-efficient paradigm to tackle this challenge, bypassing the com- plex modeling process of reinforcement learning by directly leveraging human demonstrations (Zare) al.) 2024). Moving beyond simple state-action mapping, recent advancements in learning visuo- motor policies have formulated the problem as training generative model over action sequences conditioned on observations. These approaches typically use either (1) diffusion policies. which generate behavior via conditional denoising process in robot action space (Janner et al.) [2022] Chi ct al. 2023 Ze al. 2024),, (2) autoregressive policies, which treat actions as discrete tokens and model these tokens with a GPT-like transformer (Mete et al. 2024).
State-of-the afrt----ttt--tttt- generative policies face inherent trade-offs that limit their use in closed-loop. real- time robotic control. Diffusion- based methods require multiple denoising steps per action, making them computationally slow. Consistency Policy (Prasad et al. 2024) and Flow Policy (Zhang al.) 2025a) have aimed to speed up sampling but they either require additional distillation or compro- mise sample quality. Autoregressive policies, on the other hand, sample one token per forward pass; therefore, latency scales with sequence length. Moreover, without memory, these policies lack robustness to missing observations and fail in non-Markovian tasks.
Motivated by these limitations, goal is develop generative policies that simultaneously address the inference time bottlenecks of iterative sampling and the robustness challenges inherent in non- Markovian, long-horizon manipulation tasks. For this, introduce Masked Generative Policy (MGP), which achieves low inference latency and high task success rates while supporting rapid plan edits during execution. MGP adapts masked generative transformers (Chang et al. 2022) to model latent action representations. Actions are encoded as discrete token sequences via vector
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Dynamic City Oba Missigg Liberr Long Now-Nerka
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Figure I: Overview of MGP. (1) Four properties of MGP: dynamic adaptation, flexible replanning steps, resilient execution, and global-coherent prediction. (2) Versus prior SOTA, MGP is both faster (lower per-sequence inference time) and better (higher success). (3) MGP also excels on several challenging settings: dynamic environments, abservation--issing environments, and non- Markovian, long-horizon tasks.
quantization and a conditional masked transformer reconstructs complete sequences from partial masks conditioned on observations.
We also propose two novel sampling paradigms MGP-Short and MGP-Long. MGP-Short samples action tokens in parallel with few iterative refinements, achieving high success rates and minimal latency for closed-loop control on Markovian tasks. MGP-Long integrates a novel Adaptive Token Refinement (ATR) strategy, predicting global trajectories and iteratively refining actions on-the-fly using updated observations. During refinement, informed by the new observations, our Posterior- Confidence Estimation selectively masks and corrects unexecuted tokens with low likelihood. As result, MGT-Long enables globally coherent predictions over long horizons while still being respon- sive enough to be used in closed-loop robotic control for non-Markovian tasks.
We show in Section that MGP- Short and MGP-Long achieve wate-of-theean performance on 150 manipulation tasks across three standard benchmarks under single- and multi-task training. Moreover, MGP-Long demonstrates strong adaptation and robust planning strengths in dynamic and cosseingobserrattion environments, and further excels on non-Markovian scenarios. This due to MGP-Long's goo.aagngg globally-coherent predictions conditioned on executed action tokens, dynamic confidence-drivee token updates, resilient execution under missing observations, and flexible and efficient inference via variable step sizes and targeted edits rather than full regeneration. Together, these results establish MGP as a fast, accurate, and adaptive new paradigm that enables globally- coherent predictions for visuo-motor policy learning. Our main contributions threefold:
We introduce Masked Generative Policy (MGP), the first masked generative framework for robot imitation learning, which eliminates diffusion models' inference bottlenecks and autoregressive models' sequential constraints.
For Markovian tasks, we develop MGP-Short, which adapts the masked generative trans- former for short-horizon sampling. We show that MGP-Short achieves better success rates on standard benchmarks while substantially reducing inference time.
We propose MGP-Long to enable globally-coherent predictions over long horizons, dy- namic adaptation, resilient execution under partial observability, and efficient, flexible ex- ecution. As a result, MGP-Long achieves state-of sate-of-the--ad art results on dynamic, observation missing and Markovann,, long- duration environments.
2 RELATED WORK
Diffusion Models for Robotic Manipulation. Imitation learning enables an agent to acquire an
expert policy without explicit rewards by training expert (Schaal 1996; Ho & Ermon 2016). This
carried out typically via behavior cloning (Pomerleau 1988
Torabi et al. [2018) Florence et al.
2021) inverse reinforcement learning (Ng & Russell 2000
Peng et al. 2018 Fu et al. 2018).
With the advent of deep learning, directly learning expert behaviors through time-series models has
gradually become the dominant paradigm (Zhao al. 2023
Florence al. 2019
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Recent works (Chi et al. 2023 Xian et al., 2023) have shown that diffusion models (Ho et al. 2020) can learn visuomotor policies by treating action synthesis as a conditional denoising process. Sim- ilarly, Ze et al. (2024) and Ked et al. (2024) have proposed to enhance the performance of diffusion- based policies by incorporating multimodal information such as point clouds and text instructions. Meanwhile, diffusion models have also been widely applied trajectory planning (Zheng et al.) 2025 Xiao et al., (2023). Due to the long trajectory of denoising, several studies have attempted to improve its sampling efficiency, for example, FlowPolicy (Zhang al., (025a) and Consistency Policy (Prasad et al. (2024). However, these methods either compromise sampling quality or require additional distillation.
Autoregressive Models for Robotic Manipulation The stepwise prediction of actions in autore- gressive models aligns naturally with the sequential operation of robotic task execution (Zhang al., 20256). Therefore, researchers have explored a variety of autoregressive models for imitation learn- ing (Sun et al. 2017 Xie et al.) 2020), with
meafformer-baseld architectures gradually emerging as the dominant approach due to their strong sequence modeling capacity (Cui et al. 2023: Brohan
al. 2023: Chebotar et al.) 2023 Zhao et al. 2023). More recently, large language models have extended transformer-based architectures and introduced the next-token prediction paradigm into this field, where GPT-like pipelines encode actions into discrete embeddings via tokenizer, and the transformer models the correspondence between these embeddings and observations (Shafiul- lah al. 2022). PRISE (Zheng et al., 2024), VQ-BeTT (Lee et al. (024) and QueST (Mete et al. 2024] have achieved improved task success rates and generalization by employing novel discrete representations. Other work like Chain-of-Action (Pan et al.) 2024) further extends autoregressive approaches by decoding trajectories in reverse from a goal keyframe to mitigate compounding error. However, autoregressive policies have inherent structural limitations: (i) next-token prediction leads to multi-step iterations in long-houn Long-horizon action generation, and (ii) immutability of prefixes any edit needs regenerating all subsequent tokens.
Masked Generative Transformers The masking mechanism was initially employed as a regular- ization technique in deep learning (Vincent al., 2008 Pathak et al. 2016 [ao et al. 2022 He
et al. 2022). Building on this foundation, MaskGIT (Chang et al. (022) and MUSE (Chang et al.
2023 have established masked token modeling as a scalable, text-to-image synthesis, while StyleDrop (Sohn et al., (023) demonstrates controllable, personalized generation via token-level conditioning. This paradigm has since extended beyond images. For ex-
paradigm for image and
ample, MMM (Pinyoanuntapong et al. (2024) and MoMask (Guo al., 2024) have applied masked generative modeling to long-horizon human motion.
METHODS
MGP is stochastic policy that at time samples a sequence of future actions a, RT (where T is the length of future predicted actions) given a conditioning 4, including T, past actions, robot states 8t, and visual observations O,. Here, j denotes the dimensionality of the robot action space, typically end-effector (EE) absolute position, rotation, and gripper state. In our setting, O2 represents an input observation at given t, which may comprise RGB images, Osmage Xxxxxx depth images Odepth RTyxxxxxx and/or point clouds Ope E TT x1024x3
MGP is trained by imitation learning on a dataset of expert demonstrations. We first train an Action Tokenizer that leams a discrete representation of robot action sequences (Sec. (3.1. A Masked Gen- erative Transformer (MGT) then learns to reconstruct masked sequences of action tokens (Sec. 3.2) allowing the generation of future actions conditioned on observations. Building upon MGT Chang et al. 2022), we introduce two sampling paradigms, MGP- for Markovian (Sec. 3.3 and MGP. Long for Mon-Markovins tasks (Sec. 3.4).
3.1 ACTION TOKENIZER
We use a VQ-VAE (Vnn Den Oord et al. 2017) to obtain a discrete representation of actions. Specif- ically, the VQ-VAE takes a sequence of continuous-valued actions a as input and maps this to a shorter sequence of discrete action tokens, which can be reconstructed to the corresponding actions (Fig. 1). This creates a discrete latent space for the MGT to operate over.
Action Tokenization. The tokenizer is designed to discretize actions. encodes the input actions, RTxj, into latent representation, ý E Nxd through two residual ID CNN blocks, where d
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represents the codebook dimension and N is the dimension time. For each d-dimensional vector, the closest token embedding, y E I Nxd, is then retrieved from a learnable codebook, K. [k] E IR for decoding. During decoding, the decoder employs symmetric upsampling Conv blocks to reconstruct the action sequence of length? T.
Training. The training objectives of the model follow the VQ-VAR Van Den Oord et al. 2017 which includes reconstruction loss and commitment loss:
Lvo Area || a||1 + 3 || s
where sg[-] J is the stop-gradient operator, Are is and / is 0.02. We use exponential moving averages (EMA) for codebook updates, and resetting of inactive codewords to guarantee codebook usage Chang et al.) 2022). Once trained, the weights of the VQ-VAE are frozen. During the training of subsequent MGT, the VQ-VAE employed encode the actions from the training data and to decode the tokens generated by the MGT. In the inference phase, only the VQ-decoder is used to reconstruct actions from the tokens sampled by the MGT.
3.2 MASKED GENERATIVE TRANSFORMER
MGT is required generate N future action tokens sequence [0=N= [ conditioned the observed inputs O2 and the robot's 's historical states 8, from unknown tokens by few gradual refine- ment steps. Initially, special learnable token [MASK] is introduced represent unknown tokens. In addition, [END] and [PAD] tokens are used to indicate the termination and padding of token sequence, respectively (Pinyoanuntapong al. 2024). The pipeline illustrated in Fig. 2
Structure. MGT samples all tokens in parallel, contrast to the autoregressive models like GPT. The MGT consists of two components: (1) perception encoder and (2) encoder-only transformer. The perception encoder is used to get the observation conditions' embeddings. Af- ter obtaining an O and 8t. the perception encoder encodes them through MLP layers into two feature sets, which are concatenated along the hidden dimension to construct the conditioning for the transformer. The transformer itself is composed of 2 cross-attention layers followed by 2 self- attention layers. In the cross-attention layers, the blocks compute the cross-attention map between the observation embedding and token embeddings that obtained through VQ-VAE tokenization of the ground-truth actions. The outputs of the transformer are the logits of predicted tokens.
Training. A subset of tokens is randomly masked and replaced with [MAS]] symbol. The re- maining tokens are randomly perturbed at fixed ratio by substituting them with alternative indices. The embedding of the observations O, and 8, are fed into the MGT together with the corrupted ac- tion tokens. Yxy. and MGT predicts the probabilities of tokens p(g, Yy c). It is trained to minimize the negative log-lik liliikiihood, i.c. the cross-entropy between the ground- -trth and predicted tokens:
E LMGT yEλ
log log yMo
[nE[o.N]
3.3 SHORT HORIZON MASK-AND- RAE-EEE SAMPLING (MGP-SHORE
For simple tasks, drcision-making does not rely on historical state information- can be treated as Markov decision process (MDP), and thus there no need to explicitly model long-term state dependencies. For this setting, we propose MGP-Short, a basic closed-loop inference scheme that accelerates sampling while enabling dynamic adjustments during generation based on the current state (See Fig. 3).
MGP-Short only samples the tokens conditioned on the current observation c obtained from the perception encoder. This procedure involves 2 iterations. For the first iteration, at time step f, the masked token sequence, together with er, is fed into the transformer to generate first estimate of the token logits in parallel. The n-th token index is calculated from the raw logits e,, by the Gumbel-Ma trick (Huijben et al. 2022):
y = arg max
Cn + In
In = - log lloggun)))) un ~ Uniform
(3)
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where 7 adjusts the temperature. Gumbel-Mazt ( sampling preserves modes while increasing di- versity compared with softmax sampling. Following sampling, the normalized probabilities of log- its are ranked as confidence scores, and tokens with the lowest confidence scores are re-masked (5) and regenerated during the second iteration. Parallel token generation, combined with a log- bikelihood-based masking strategy enable high-quality token generation within only few iterations.
3.4 LONG HORIZON MASK-AND-RENEE REFINE SAMPLING (MGP-LONG)
To address limitations of existing methods in terms of efficiency and long-horizon tasks, we extend MGP to long-horizon action generation, MGP-Long. Through Sosptive-Toke--Reinneenn (ATR), MGP-Long samples an initial nearly full episode action sequence given the initial observation and begins executing this; then during the rollout, it progressively refines the cet-to-be-xxecuter action tokens as new observations arrive, while retaining executed actions (Fig. 3). Specifically, posterior- confidence estimation enables the model to continuously update confidence score using current observations and historic states.
During inference, MGP-Long predicts action tokens in parallel, similarly to MGP-Short. At the beginming of a task, based on the initial observation condition co. MGP-Long infers the conditional probability p(yb-N (co) of token and samples the complete sequence of tokens yo-N covering the entire task horizon as its initial prediction. The robot can then be directed to execute tokens with freely adjustable step length. After the i-th execution of tokens, with token y executed and y.- still pending, an updated observation ej is received from the environment and the subsequent tokens are updated in response to the new observation.
Posterior-confidence estimation. To ensure efficient utilization of generated tokens while main- taining global-coherence, we introduce Posterior-comfidence estimation, a novel masking and refine- ment mechanism for the Adaptive-Tokenn Token-Refinement. Suppose that the hidden state for this process H. is provided by the tokens that have already been executed. The transformer will compute the new probability of the previously sampled result under the new observation G as preliminary confidence score as:
S(yP-N) p(y.-Y(Ga,,... H4-1)) softmax(e""
this is analogous finding updated Bayesian posterior predictive distribution given the new observation. Since the historical tokens have already been executed, their scores should be excluded from computation. Only the scores of tokens from n N are normalized, and tokens with low scores are re-masked based on the ranking.
Y--M + MASK(y,Y. SSy?"\
(5)
The re-masked tokens, together with the previously executed historical tokens, are then fed back into the transformer for refinement, yielding the updated token logits:
N:N GumbelMax(p(y?"" n:N
O:N Ni-IM' C₁ H1:
(6)
Afterward, the token indices are sampled by the Gumbel-Man trick (6) and then decoded into actions by VQ-VAE. Since historical tokens are retained in the recursive generation process and incorpo- rated into the refinement of subsequent tokens, the model is able to preserve memory of previously executed steps.
4 EXPERIMENTS AND RESULTS
In this section, we first evaluate on three standard benchmarks, Meta-World (Yu et al.] 2020). LIBERO-90 (Liu et al. 2023), and LIBERO-Long, covering both short- and long-duration tasks un- der single-task and multi-task training (Section[4.]) Beyond these, we have designed three challeng- ing evaluation environments that are important for long-horizon control and have repeatedly chal- lenged prior methods: observation missing environments (Section 4.2); dynamic environments with moving objects/obstacles (Section 4.3); and two onngduration non- non-Markovian tasks (Section 4.4) Finally, we conduct four ablation studies (Section 4.5
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Stage Training Action Tokenizer
Stage Training Mask Generative
Stage Sharf-Horizon Sampling (MGP-Short)
0g
4gg
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Action
Caay
Enceder
Actonne: Tolkenzzo
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12
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Label
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Figure 2: Left: Training Stage 1 Action Tokenizer and Middle: Training Stage 2 - Masked Gener- ative Transformer and Right: Short-horizon sampling (MGP-Short)
Long- Hong-Horion Sampling (MGP-Long)
Attian Stop
Action Step
MCT
MGT
Pastelier Confidence Buteeation
Drcnder
MGT
Masiend Roudls
Figure 3: Long-horizon sampling (MGP-Long) through Adaptive Token Refinement (ATR).
Model variants. In addition MGP-Short and MGP-Long, we include ablations of MGP- Long: (1) Full-Horizon MGP (MGP-Full seq.), which generates the full trajectory once without dynamic online adaptation; and (2) MGP-LLog without score-bused mask (MGP-w/o SM), which replans by masking all remaining tokens whenever new observations arrive and regenerating the remainder from scratch. These two ablations of MGP-Long separate: (1) the benefit of planning the whole sequence, (2) the need for targeted edits for efficiency and stability, and (3) the advantage of preserving unmasked tokens to maintain global consistency across the trajectory.
Baselines. We evaluate against ten baselines: diffusion methods-Diffiue 2023), 3D Diffusion Policy (include simple DZ3 et al. 2024)- two accelerated diffusion meth- ods (Consistency Policy(Prasad (Prasad et al., 2024) and FlowPolicy (Zhang et al. 2025a)) targeting faster sampling; and autoregressive methods SMeee PRiSE/Zheng et al., 2024)), ACT et al. (023). and ResNet-T(Liu et al. 2023). Beyond the standard baselines, we also evaluate Full-Horizon DP3 (DP3-Full Seq.), diffusion baseline that plans the entire action sequence from the initial observation and executes it open-loop (no replan- ning), similar to MGP-Full Seq.
Policy (Chi et al.
al. (0224), VQ- D--eeT@Lee et al.) 2024),
All comparisons use identical visual encoders and demonstration numbers, ensuring that improve- ments are from MGP-Short and MGP-Long. Implementation details of standard benchmarks are shown in Appendix Sec. A.3
4.1 STANDARD BENCHMARKS
Separate Training on World. We run single-task experiments on all 50 Meta- World tasks spanning Easy to Very Hard (Seo et al. 2023). We use only short horizon methods the Easy and Medium tasks, but long horizon on Hard and Very Hard tasks that benefit from a longer planning window. Ten expert demonstrations per task are generated using Meta-World heuristic policies. We

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Published as a conference paper at ICLR 2026
Table 2: Success Rate (SR) and Inference Time per step (Inf.T) of Single-Task Training on Meta- World. 'Inf. T per step' represents inference time per step (ms/step). 'Inf. 'T per seq.' represents inference time per sequence (ms/sequence).
Mela-World
Methods
Eacy
Mediam
Hard
Very Hard
Avg.
Avg. Inf. per step
per sey.
Simple-DP3
0.350
28:30
DPS
0,909
0.616
0.380
0.490
0.599
145
6657
CP
0.912
0.627
0.400
0.510
0,11
230 19
5
FlowPolicy
0,902
0.630
0.392
0.360
0.571
850
MGP (ours)
0.920
0.650
0,400
0.538
0,637
135
evaluate 20 episodes every 1000 iterations using seeds. We compute the average of top-5 success rates per task, per-step inference latency (ms) and per-sequence inference latency (ms).
In Tab. 2 we see that MGP-Short achieves state-of-the-art performance across the 50 Meta-World tasks for all difficulty levels, with an overall average success rate of 0.637, 3.8% higher than DP3 and 6.6% higher than FlowPolicy. MGP-Shor takes just ms per step, which is 49x faster than DP3 (145 ms). Notably, it also faster than CP and FlowPolicy, without their additional distillation or approximations.
In Tab. D we show results of long-horizon methods on ten Hard and Very Hard tasks. MGP-Long improves over MGP-Short by about 10% on Hard and approximately 5% on Very Hard, and for DP3, by 16% and about 10%, respectively, while reducing sequence-level latency from 135 ms (MGP-Short) and 6,557 ms (DP3) to 80 ms.
Compared with other long-horizon base- lines, MGP-Long outperforms DP3-FullSes, by 35.2% and 23.6%, respectively. Relative to the two ablations, MGP-FullSeq and MGP- w/o-SM, MGP-Long further raises success by 22.3% and 2.2%, respectively, demonstrat- ing the benefit of dynamic, targeted refine- ments, and the advantage of preserving high- confidence subsequences as anchors while up- dating only uncertain tokens. Detailed results are shown in Appendix Sec. C.I.1
Table 1: Success Rate of Long-horizon methods under Single-Tast Training on Meta- World.
Methods
Hard (5)
Very Hard (5)
Avg. SR
DP3-Full Seq.
0.188
0.350
0.270
MGP-Ful Seq.
0.294
0,386
0.340
MGP-w/o SM
0.510
0.572
0.541
MGP-Long (ours)
0.540
0.586
0.563
Multi-task training on LIBERO-90
We evaluate the multi-task -mitation-learning capa- bility of MGP-Short on LIBERO-90, suite of 90 language-conditonn on MGP-Short here because LIBERO-90 fea- tures short-horizon tasks. Following QueST, we use 50 demonstrations per task and train single multi- task policy. Each task is evaluated on 50 held-out episodes from a predefined set.
Table 3: Success Rate of Multi-Tann Training on Libero90 and Libero-Long.
manipulation tasks. We fo-
Methods
Libero90
Libero-Long
ResNet-T
0.844
0.441
ACT
0,508
DP
0.754
0.501
PRISE
0.544
Results are shown Tab. 3 MGP-Short achieves an average success rate of 0.889, comparable QueST (0.886) and exceeding all other basclines including 13.5% higher than DP and 7.6% higher than VQ- BеT. Crucially, it achieves this accuracy with sub- stantially lower per-step inference latency, reducing time from 17 ms to 5 ms relative to QueST, because it predicts tokens in parallel and uses only one or two selective refinement passes rather than token-by-token decoding. Detailed results of tasks shown in Appendix Sec. C.1.2
VQ-BeT
0.813
0.593
Quest
0.886
0.680
MGP-Short (ours)
0.889
0.770
MGP-w/o SM
0.805
MGP-Long (ours)
0.820
Multi-task training Long-duration Libero-Long
We evaluate MGP-Short and MGP-Long on long- cong-duration manipulation using LIBERO-Long in the multitask setting. We follow the evaluation protocol and training and inference as Section 4.1 As
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Table Success Rate of Single-Task Training on Observation missing and Dynamic environments.
Methods
Obs. Missing Wlefd-World
Dynamic Meta-World
Hard(5)
Very Hard(5)
Avg.SR
Baskethall
Pick place wall(W)
Pick place walk()
Pash
Pash walk(T)
Avg-SR
DP3
0,160
0.240
0.200
0.91
0.35
0,07
0.20
0.27
0.360
MGP-Short (ours)
0.172
0.238
0.92
0.40
0,15
0.430
DF3-Full Seq.
0.188
0.350
0.269
0.02
0,10
0.00
0.05
0.03
0.040
MGP-Full Horizon
0.294
0.386
0.340
0.05
0.13
0.05
0.09
0.20
0.100
MGP-wwo
0.416
0.538
0.25
0.12
0:396
MGP-Long (ours)
0.484
0.566
0.525
0.3
0,13
0.25
0.50
0.436
shown in Tab. B MGP-Short achieves an average success rate of 77%, outperforming QueST by 9% and reducing the per-step inference latency from 16.3 ms per step to 4.5 ms. These gains indi- cate that the masked- masked-generativv generative framework is well-suited to long-duration tasks because it mitigates the sequential bottleneck of autoregressive decoding and reduces compounding errors by captur- ing longer-range dependencies. MGP-Long further improves performance, reaching 82.0% average success, increasing success by 5% over MGP-Short, The ablation MGP-w/o-SM reaches 80.5%, lower than MGP-Long by 1.5%, confirming the benefit of score-based confidence masking. In addi- tion, MGP-Long achieves the fastest sequence-lewe inference time, dropping from 225 ms to 78 ms compared with MGP-Short Detailed results are shown in Appendix Sec C.I.3
Model Size and Speed
Our two-stage system is lightweight and fast: 7M parameters (37x fewer than DP3 Ze et al. (2024)'s 's 262M); training for 2000 epochs takes 55 minutes (10 minutes for Stage one and 45 minutes for Stage two) vs. 3 hours on the same RTX 4090 setup. See Appendix Sec. C.ó details.
4.2 ROBUSTNESS TO MISSING OBSERVATIONS
To evaluate robust action continuation under partial observability, we test with observation dropouts. At each control cycle during inference, the current observation is withheld with some probability, forcing the policy to execute actions without fresh sensory input. We use 10 Meta- Hard/Very Hard tasks; training follows Section [.1 For short-horizon baselines (DP3, MGP-Short), when dropout occurs, the controller holds position (zero-action/hold) until the next observation arrives, then generates a new action clip. In contrast, MGP-Long continues executing its already planned actions from the initial full-horizon proposal and previous steps refinement and skips the score-based masking scheme while observations are missing until sensing resumes.
Results in Tab. 4 (left) show MGP-Long achieves an average success of 0.484 on Hard and 0.566 on Very Hard, gains of roughly 22%-31% over the short-horizon methods. Short-horizon policies failed because dropouts yield static, contrast, MGP-Long follows full-horizon planning and retains the high-confidence future tokens as anchors, making it resilient to partial observability than windowed clip decoding. We further evaluate MGP-Long as the observation-drop probability increases from 0.35 to 0.70 and find that remains robust missing observations. Detailed results provided in Appendix Sec. C.2
point clouds with little motion signal. By
4.3 DYNAMIC ENVIRONMENTS
We construct dynamic variants of five Meta- World tasks. in which key scene elements move contin- uously during execution: a translating hoop in Basketball, a moving wall in Pick Place Wall-Wall, a drifting goal in Pick Place Wall-Target, and moving targets in Push and Push (Wall).
Results are shown in Tab. (right). Existing full-sequence methods tend to fail: DP3-FulISeq achieves an average success of 0.04 and MGP-FullSeq 0.10, indicating that one-shot, open-loop rollouts cannot cope with continual scene changes. contrast, MGP-Long delivers the best per- formance with 0.436 average success. MGP-Long achieves marginally better performance than MGP-Short (0.43; +0.6%), and both clearly surpass DP3 (0.36; +7.6%). These results demonstrate MGP-Long's dynamic adaptation: it maintains globally coherent trajectory while performing quick in-place refinements to track moving targets and avoid shifting obstacles. Detailed environments setup and qualitative results are in Appendix See A.4 and Sec.
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4.4 NON-MARKOVIAN ENVIRONMENTS
To assess MGP-Long's globally-coherent predictions, we design two non-Markovian tasks. Both simulate an 80x80em tabletop with a rail forming loop along the perimeter; a LeRobot $0101 is mounted carriage that follows the rail. Push-buttons are placed near the four table comner. In Button Press On/Off, each button is lit red, green, blue or yellow. For each episode, colors are randomly assigned to corners and all lights are on. The robot must press buttons in fixed color order such as red + green - blue + yellow, independently of the button's 's location. The scene looks identical after any button press, so progress is unobservable from single frame. In Button Press Color Change, each button cycles through five states when pressed: yellow + red - green + blue - off. The four buttons start with different colors. The task press buttons in the order of their initial colors. Thus multiple buttons may display the same color during execution (e.g. after pressing the green button, there will be two blue buttons), and identical frames can correspond to different stages. See Appendix Sec. A.3 for detailed descriptions and dataset collection procedures. To evaluate, we use 20 rollouts and report the success rate.
In Tab. []] MGP-Long achieves the highest success on both non-Markovian button tasks. In both tasks, short-horizon methods (DP3, QueST, MGP-Short) often fail to complete the sequence, because identical frames provide observable progress signal. By contrast, MGP- Long maintains full-horizon plan and per- forms confidence- guided,, in-place refinements, enabling it to complete the prescribed color order.
Table Success Rate on two Non- Markovian en- vironments.
Method
Button Pross Ow/Of!
Rutton Press Change Color
DP3
0.00
0.00
QueST
0.00
0.00
MGP-SSoolt (ours)
0,00
0.00
MGP-I oing (ours)
1.00
1.00
Qualitative results see Appendix Sec. C.4
4.5 ABLATION STUDY AND HYPERPARAMETER EXPERIMENTS
We conduct seven ablation and hyperparameter experiments. Full results are given in Appendix B
Refine steps for MGP-Short: On five hard Meta-world tasks, increasing mask-refine iterations from r = to r = 2 yields a gain of 14.3%, validating the score-based masking scheme, while r 3 does not offer a significant benefit and adds latency; therefore, we adopt r 2 as default.
Codebook size for MGP-Short: We explore the influence of codebook size on five World Very Hard tasks, training separate models with codebook sizes of 512, 1024, and 2048. The average success rates are 0.534, 0.538, and 0.522, respectively. The gaps are small (1.6%) and show no consistent trend, indicating minimal sensitivity to codebook size.
We test with 2 actions/token and 8 actions/token on the five Meta- World Very Hard tasks. Averaged over tasks, 4 actions/token achieves the highest suc- cess rate (0.538) outperforming 2 actions/token (0.526, +1.2%) and 8 actions/token (0.514, +2.4%). The gaps are modest, indicating limited sensitivity to granularity.
Discretization granularity for MGP-Short:
We evaluated E {1, 2,3) on five dy- namic tasks of MGP-Long. Increasing the refinement steps from r = 1 to r = 2 raises the success rate by +5.2%, indicating that the score- score-based masking scheme is helpful in more challenging set- tings. Increasing further from r = 2 to r = 3 yields < 1% improvement, while incurring higher inference cost. We therefore set
Refine steps for MGP-Long in challenging environment:
for MGP-Long in the main experiments.
Mask ratio for MGP-Long: We vary what proportion of low confidence tokens are resampled. When replanning we rank the unexecuted tokens by posterior confidence and in our standard setting mask the bottom 70% for one refinement pass. Changing between 50%, 70%, and 85% shows that 70% yields the best average success across five Meta-World Very Hard tasks. A 50% ratio underperforms because many low-confidence tokens remain unedited and can also interfere with accurate regeneration of the masked ones; 85% is comparable to 70% on average.
Scoring policy for MGP-Long: At each replanning step f, we ablate three confidence-updann schemes choose which future tokens to re-mask first prior refinement: (1) Random: Use ran- dom scores to mask the rest of tokens, (2) Score Reuse: Reuse the previous confidences from last

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steps to select the lowest-confidence tokens; and, (3) ATR: Recompute current confidences from the latest observations using the masked generative transformer first, then select only the low-confidence tokens. ATR attains the highest success, 10.68% over Random and 5.53% over Score Reuse.
Varying execution step for MGP-Long: On five hard Meta- Woold tasks, we evaluate MGP-Long across execution step sizes of 4, 12, and 36. A step size of 12 yields the highest success (54%), while steps of 4 and 36 achieve 47.8% and 48% respectively, and all settings outperform MGP-Short.
4.6 CONFIDENCE SCORE ANALYSIS
During inference, MGP uses confidence score-based masking (Fig. 2 and Fig. this behaves in practice, we conducted two experiments. See Appendix Sec. C.5 for details.
30.
To examine how
First, we visualize token-wise confidence across refinement passes actions are executed for MGP- Long in both the Meta-World and a dynamic environment. We show (i) a confidence heatmap and (ii) matched mask-unmask map that marks which tokens are edited at each pass. On MetaWorld Dis- assemble (quasi-static, "Very Hard"), confidence remains high during approach phases but becomes low during precise, outcome-critical manipulations and at actions requiring repeated attempts- for example, it stays high while the gripper approaches the ring, then falls when the gripper must po- sition itself accurately to grasp and lift, especially when the first grasp fails and the policy makes repeated adjustments. On dynamic Basketball with a moving hoop, confidence is high while the hoop is stationary and drops once the hoop begins moving. Thus, drops in confidence typically align with environment changes, and refinement focuses on edits exactly where they matter.
Second, we assess calibration by resampling low- vs. high-confidence tokens on 'Disassemble' task in Metaworld. Masking the lowest 70% yields 0.86 success and 60.6% flip rate (fraction of masked tokens that change after refinement), whereas masking the highest 70% reduces success and drops the flip rate to 15.1 %, indicating that the scores reliably target uncertain tokens.
REAL WORLD EXPERIMENTS
To evaluate global planning in the real world, we deploy MGP and DP3 on a non-Markovian towel- sorting task. A towel and two baskets are placed in the robot's working area with a random lateral offset. At the beginning of each episode, light briefly turns on in either red or blue and is then switched off; the color indicates which basket the towel should be placed in. The robot must grasp towel and place it into the correct basket based solely on the initial light color. As the light is only visible at the very start of the episode, later observations do not contain any information about the desired goal, making the task non-Marte Mark-vinn...n. We collect 60 expert demonstrations by teleoperation of the LeRobot arm. For each trial, we record robot joint positions, end-effector position and ori- entation, gripper state, and synchronized RGB, depth, and point-cloud data. We evaluate model in 25 real-world trials. Since short-horizon models such as DP3 and Quest are incapable of solving non-Markovian tasks (as shown in Sec. 4.4) adopt DP3-FulISeq as the baseline. MGP-Long achieves a success rate of 96%, outperforming DP3-Full Seq (which achieves 84%). Moreover, MGP-Long maintains the same success rate when observations are missing during action execution. These results show that MGP-Long is robust to complex and noisy real-world conditions, and re- tains its strong global reasoning capabilities in this setting. See Appendix Sec. foor implementation details and qualitative results.
6 CONCLUSION
We have introduced Masked Generative Policy (MGP), the first masked-generatte framework for visuomotor imitation learning, and two variations. MGP-Short for Markovian control and MGP- Long for complex and Mar-Markvvian tasks. MGP-Short combines the sample diversity of diffusion methods with the low latency of autoregressive models, and delivers extremely rapid inference and improved success rates as evidenced section 4 MGP-Long produces a full trajectory in one pass and then continuously refines future actions as new observations arrive; this enables global reason- ing, dynamic adaptation, robustness to missing observations, and efficient and flexible replanning. Overall, our results position MGP a fast, accurate, and flexible alternative to diffusion and vi- suomotor policies. However, MGP requires two-stage training, introducing potential mismatch and increased complexity. This limitation is left for future work.
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7 REPRODUCIBILITY STATEMENT
Experiment details, including all hyperparameters and implementation details, are in Appendix with further results in Appendix Sec. These materials enable full reproducibility.
Sec. A
ACKNOWLEDGEMEENTS
This research has been supported by EPSRC Grant No. EP/S019472/1.
REFERENCES
Hangbo Bao, Dong, Songhao Piao, and Furu Wei. BEit: BERT pre-training of image trans-
formers. In International Conference Learning Representations, 2022. URL https:
//openreview. www.
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil Joshi, Ryan Julian, Dmitry Kalash- nikov. Yuheng Kuang. Isabel Leal, Kuang- Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deek- sha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag R Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vin- cent Vanhoucke, Steve Vega, Quan H Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, and Brianna Zitkovich. RT-1: Robotics Transformer for Real-World Control at Scale. In Proceedings of Robotics: Science and Systems, Daegu, Republic of Korea, July 2023, doi: N0.15607/RSS.2023..10 2003XXXX0222
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman. Maskgit: Masked generative image transformer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1311--1325,,, 2022.
Huiwen Chang, Han Zhang, Jarred Barber, AJ Maschinot, José Lezama, Lu Jiang, Ming-Hsuan Yang, Kevin Murphy, William T Freeman, Michael Rubinstein, et al. Muse: Text-to-image gen- eration via masked generative transformers. Proceedings of the 40th International Conference Machine Learning, pp.
2023.
Yevgen Chebotar, Quan Vuong, Karol Hausman, Fei Xia, Yao Lu, Alex Irpan, Aviral Kumar, Tianhe Yu, Alexander Herzog. Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Anand Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deek- sha Manjunath, Jaspiar Singh, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, and Sergey Levine. Q-transformer: Scalable offline reinforcement learning via autoregressive q- functions. In 7th Annual Conference Robot Learning, 2023.
net feet/forum?in 013su3mkuL
Cheng Chi, Siyuan Feng, Yilun Du, Zhenjia Xu, Eric Cousineau, Benjamin Burchfiel, and Shuran Song. Diffusion policy: Visuomotor policy learning via action diffusion. Robotics: Science and Systems, 2023.
Zichen Jeff Cui, Yibin Wang, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. From play to policy: Conditional behavior generation from uncurated robot data. In The Eleventh International Conference Learning Representations, 2023.
7 1
Pete Florence, Corey Lynch, Andy Zeng, Oscar A Ramirez, Ayzaan Wahid, Laura Downs, Adrian Wong, Johnny Lee, Igor Mordatch, and Jonathan Tompson. Implicit behavioral cloning. In 5th Annual Conference on Robot Learning, 2021. URL. htt.s:tpp//// www.revvieeeeeeo net fforrm?
id=ri F3a5NAxU6
Peter Florence, Lucas Manuelli, and Russ Tedrake. Self-supervised correspondence visuomotor policy learning. IEEE Robotics and Automation Letters, 5(2):492 499, 2019.

---

Published as a conference paper at ICLR 2026
Justin Fu, Katie Luo, and Sergey Levine. Learning robust rewards with adverserial inverse rein- forcement learning. International Conference on Learning Representations, 2018.
Chuan Guo. Yuxuan Mu, Muhammad Gohar Javed, Sen Wang, and Li Cheng. Momask: Gener- ative masked modeling of 3d human motions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1900-1910, 2024.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked au- toencoders scalable vision learners. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16000-16009, 2022.
Jonathan Ho and Stefano Ermon. Generative adversarial imitation learning. Advances in neural information processing systems, 29, 2016.
Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. In Proceed- ings of the 34th International Conference on Neural Information Processing Systems, NIPS '20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546
Iris AM Huijben, Wouter Kool, Max B Paulus, and Ruud JG Van Sloun. review the gumbel- max trick and its extensions for discrete stochasticity in machine learning. IEEE transactions on pattern analysis and machine intelligence, 4552):135,1131,, 2022.
Michael Janner, Yilun Du Joshua Tenenbaum, and Sergey Levine. Planning with diffusion for flexible behavior synthesis. In International Conference on Machine Learning, pp. 9902-9915. PMLR, 2022.
Tsung-Wa Wei Ke, Nikolaos Gkanatsios, and Katerina Fragkiadaki. 3d diffuser actor: Policy diffusion with 3d scene representations. arXiv preprint arXiv:2402.10885, 2024.
Seungjae Lec, Yibin Wang, Haritheja Etukuru, H Jin Kim, Nur Muhammad Mahi Shafiullah, and Lerrel Pinto. Behavior generation with latent actions. In Proceedings of the 41st International Conference on Machine Learning, pp. 26991-27008, 2024.
Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng. Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning. Advances in Neural Information Processing Systems, 36:44776-444 44791, 2023.
Atharva Mete, Haotian Xue, Albert Wilcox, Yongxin Chen, and Animesh Garg. Quest: Self- supervised skill abstractions for learning continuous control. Advances Neural Information Processing Systems, 37:4062-4089, 2024.
Andrew Y. Ng and Stuart Russell. Algorithms for inverse reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning, ICML '00, pp. 663-670, San Francisco, CA, USA, 2000. Morgan Kaufmann Publishers Inc. ISBN 1558607072.
Zhenyu Pan, Haozheng Luo, Manling Li, and Han Liu. Chain-of-action: Faithful and multimodal question answering through large language models. CoRR, 2024.
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.
2016.
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer, Pieter Abbeel, and Sergey Levine. Variational dis- criminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining infor- mation flow. In International Conference on Learning Representations, 2018.
Ekkasit Pinyoanuntapong. Pu Wang, Minwoo Lee, and Chen Chen. Mmm: Generative masked motion model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1546-1555, 2024.
Dean A. Pomerleau. Alvin:: An autonomous land vehicle in a neural network. In D. Touretzky (ed.), Advances in Neural Information ProiiiinnPPrreee Processing Systems, volume 1. Morgan-Kaufman 1988.
12

---

Published as a conference paper at ICLR 2026
Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou, and Jeannette Bohg. Consistency policy: Accel- erated visuomotor policies via consistency distillation. arXiv preprint arXiv:2405.07500 2024.
Stefan Schaal. Learning from demonstration. Advances in neural information processing systems, 9, 1996.
Younggyo Sco, Danijar Hafner, Hao Liu, Fangchen Liu, Stephen James, Kimin Lee, and Picter Abbeel. Masked world models for visual control. In Conference on Robot Learning, pp. 1332- 1344. PMLR, 2023.
Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui, Ariuntuya Altanzaya, and Lerrel Pinto. Behavior transformers: Cloning SkS modes with one stone. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (eds.), Advances in Neural Information Processing Systems, 2022.
URL /tttps:://ppereeveew net/ net/ffoumm forum? D
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee, Daniel Castro Chin, Irina Blok, Huiwen Chang, Jarred Barber, Lu Jiang, Glenn Entis, Yuanzhen Li, al. Styledrop: Text-to-image generation in any style. In 37th Conference on Neural Information Processing Systems (NewrIPS). Neural Informa- tion Processing Systems Foundation, 2023.
Wen Sun, Arun Venkatraman, Geoffrey Gordon, Byron Boots, and Andrew Bagnell. Deeply ag- grevated: Differentiable imitation learning for sequential prediction. In International conference on machine learning. pp. 3309-3318. PMLR, 2017.
Faraz Torabi, Garreu Warnell, and Peter Stone. Behavioral cloning from observation. In Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI18, pp. 4950-4957. AAAI Press, 2018
Aaron Van Den Oord, Oriol Vinyals, al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017.
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoing Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, pp. 1996-1103, 2008.
Zhou Xian, Nikolaos Gkanatsios, Theophile Gervet, Tsung- Wei Ke, and Katerina Fragkiadaki. Chaineddiffuser: Unifying trajectory diffusion and keypose prediction for robotic manipulation. In 7th Annual Conference on Robot Learning, 2023. URL https: / /eppppnreee reviev net.
Tsung-Wei
Forum? 30000gg222TAABB
Wei Xiao, Tsun-Hsure Wang, Chuang Gan, Ramin Hasani, Mathias Lechner, and Daniela Rus. Safediffuser: Safe planning with diffusion probabilistic models. In The Thirleenth International Conference on Learning Representations, 2023.
Fan Xie, Alexander Chowdhury, M De Paolis Kaluza, Linfeng Zhao, Lawson Wong, and Rose Yu. Deep imitation learning for bimanual robotic manipulation. Advances in neural information processing systems, 33:2327-2337, 2020.
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine. Meta- world: A benchmark and evaluation for multi-task and meta reinforcement learning. In Conference on robot learning, pp. 1094-1100. PMLR, 2020.
Maryam Zare, Parham M Kebria, Abbas Khosravi, and Saeid Nahavandi. A survey of imitation learning: Algorithms, recent developments, and challenges. IEEE Transactions Cybernetics, 2024.
Yanjie Ze, Gu Zhang, Kangning Zhang, Chenyuan Hu, Muhan Wang. and Huazhe Xu. 3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations, arXiv preprint arXiv:2403.03544, 2024.
Qinglun Zhang. Zhen Liu, Haoqiang Fan, Guanghui Liu, Bing Zeng, and Shuaicheng Liu. Flow- policy: Enabling fast and robust 3d flow-based policy via consistency flow matching for robot manipulation. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp. 14754-14762, 2025a.
13

---

Xinyu Zhang, Yuhan Liu, Haonan Chang, Liam Schramm, and Abdeslam Boularias. Autoregressive action sequence learning for robotic manipulation. IEEE Robotics and Automation Letters, 2025b.
Tony Z Zhao, Vikash Kumar, Sergey Levine, and Chelsea Finn. Learning fine-grained bimanual manipulation with low-cost hardware. arXiv preprint urXiv:2304.13700 2023.
Ruijie Zheng, Ching-An Cheng, Hal Daumé III, Furong Huang, and Andrey Kolobov. Prise: Learn- ing temporal action abstractions as sequence compression problem. CoRR, 2024.
Yinan Zheng, Ruiming Liang, Kexin Zheng, Jinliang Zheng, Liyuan Mao, Jianxiong Li, Weihao Gu, Rui Ai, Shengbo Eben Li, Xianyuan Zhan, et al. biffusion-base planning for autonomous driving with flexible guidance, arXiv preprint arXiv:2501. 15564, 2025.
Appendices
Experiment Details
16
A.1
Hyperparameters
16
Standard Benchmarks
16
A.3
Implementation Details of Standard Benchmarks
17
A.3.I
Separate Training in Metaworld
17
A.3.2
Multitask Training in Libero90)
17
A.4
Dynamic Environments Set
17
A.5
Markovian environments
18
A.3.I
Task Description
18
A.5.2
Datasel
18
A.5.3
Implementation Details
18
Ablation Studies and Hyperparameter Experiments
18
B.
Refine step for MGP-Short
................................
19
B.2
Codebook size for MGP-Short
19
B.3
Discretization granularity for SGorShort
19
B.4
Refine step for MGP-Long under challenging conditions
19
B.5
Mask ratio for MGP-Long
19
B.6
Score policy for MGP-Long
20
B.7
Varying execution step
20
Additional Simulation Results
20
Evaluation on Standard Benchmarks
20
C.I.I Single task training of MetaWorld
20
C.1.2
Multitask training of Libero90
C. 1.3
Multitask training of Liberol0
21
Evaluation Observation-Missing Environments
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C.3
Evaluation on Dynamic Environments
23
Evaluation on Non- Markovinn Environments
23
C.S
Confidence score analysis
28
Model Size and Speed
28
Accuracy of Tokenized Actions
28
Multimodality analysis
28
D Real-Worla Experiments
31
D.1 Implementation Details
D.2 Qualitative Results
31
Use Large Language Models (LLLM))
32
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A EXPERIMENT DETAILS
A.I HYPERPARAMETERS
As for MGP-Short, Tab. and Table present the stage and stage of MGP-Short in Meta- World benchmark and in LIBERO benchmark.
As for MGP-Long, Tab. presennss stage and stage two of MGP-Long in Meta- Meta-World benchmark, observation missing environments, dynamic environments and Non-Markovian environments.
Table 6: Stage of MGP-Short in Reta-World and Libero Benchmark
Meta-World
gamma
0.1
0.1
commit
0.02
0.02
code dim
16
nb of code
1024
8192
down sampling rate
stride size
2
horizon
32
Table 7: Stage 2 of MGP-Short in Meta-World and Libero Benchmark
Parameter
Meta- World
Jibero90&Libber---.
gamma
0.1
weight decay
le-6
le-6
code dim
16
16
number of code
1024
8192
downsamping rate stride size
2
black size
54
68
cross attention layer
self attention layer embedding dimension
256
512
horizon
32
observation history
execution horizon
8
A.2 STANDARD BENCHMARKS
Meta-Worke (Yu et al., 2020) is a simulated benchmark with broad of robotic manipulation tasks. We use the official difficulty classification that ranks tasks from easy to very hard (Seo et al.) 2023). Each demonstration contains 200 timesteps. In our experiments, we train 50 tasks on Meta- World separately.
We use LIBERO's language-conditioned manipulation suites simulation (Liu et al. 2023 LIBERO-90 contains 90 single-goal, short-horizon tasks spanning diverse scenes (kitchen, living room, study) with rigid and articulated objects. Each task accompanied by demonstrations (50 per task) with multi-modal observations (RGB views and proprioception). LIBERO-LONG is long- duration benchmark which comprises 10 horgzhorizon compositions, each formed by sequencing two LIBERO-90 goals, requiring multi-stage execution.
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Table Stage and Stage 2 of MGP-Long in Missing-obs, Dynamic and Non-Markovian Environ- ments
Stage one Parameter
Variant Environments
Stage two Parameter
Variant Environments
gamma
0.1
gamma weight decay dim number code
0.1
commit
0.02
le-6
code dim
16
16
code
1024
1024
down sampling rate
downsamping rate stride size block size cross attention layer self attention layer
2
stride size
2
2
horizon
128
54
2
2
emhedding dimension observation history execution horizon
256
4
12
A.3 IMPLEMENTATION DETAILS OF STANDARD BENCHMARKS
A.3.1 SEPARATE TRAINING IN METAWORLD
As for the implementation details MGP-short, we train a VQ- MAE tokenizer that maps every four consecutive primitive actions to single discrete token in Stage 1. In Stage 2, conditional masked transformer predicts two tokens (8 actions) in parallel, conditioning on observation features obtains from the same visual encoder as DP3 via cross-atiention. At inference, MGP-Short conditions on the four most recent observations, predicts two tokens in parallel, performs two refinement iterations, and then executes the last five primitive actions before replanning.
As for implementation details of MGP-Long, the VQ-VAE tokenizer is first trained to map every 4 consecutive primitive actions to one discrete token over 128 actions. Afterwards, the decoded token sequences are truncated with different lengths. Among them, 70% retain the original length, while 30% are assigned a randomly sampled token length. The remaining training procedure identical to that described in the main text. When inference, based on the initial four observations, MGP-Long predicts a full-horizon token sequence then decodes and executes the next 12 primitive actions via the VQ decoder. After receiving the up- dated observations, the model recomputes confidence scores for the remaining tokens conditioned on the latest observations the initial scores, then re-masks only the low-confidence subset (exe- cuted tokens and high-confidence tokens remain fixed), performs one selective refinement pass, and again decodes and executes the next 12 actions. This loop repeats until termination, yielding full- trajectory planning with efficient, on-the-fly adjustments while avoiding unnecessary regeneration of confident token segments.
single forward pass (we use 50 tokens per episode).
A.3.2 MULTITASK TRAINING IN LIBERO90
Training follows Sec A.3.1 A VQ-VAE tokenizer compresses 4 consecutive primitive actions to single discrete token. is followed by conditional masked transformer condition on observation and task features via cross- attention, using the same encoders as QueST, and predicts 8 tokens (32 actions). At inference, MGP-Short conditions on the most recent observations, predicts tokens in parallel, performs two refinement iterations, and then executes the last 8 primitive actions.
A.4 DYNAMIC ENVIRONMENTS SET UP
Five dynamic environments are designed based Meta-World benchmark.
Pick-Place- Wall (moving target) In pick-place-war wall environments, the goal site moves along +- or - with equal probability by 0.14 m from steps 1-67 (~ 6.7 s; z 0.021 m/s), then remains fixed.
Basketball (moving net) In basketball environment, the hoop body basket goal moves along +x by 0.10 m from steps 0-100 (~ 10 0.010 m/s), then stays fixed.
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Pick-Place-Wazd Wall (moving wall) In pick-place place-wall environment, the body wall moves continu- ously along -z for the entire episode with total offset 0.20 m (steps 0-200; = 0.010m/s)
Push-Wall In push wall environment, the goal and wall translate together along + by 0.08 m over steps 1-67 (~ 6.7 0.012 m/s), then stop.
Push In push environment, the goal marker (and object ob ob jGeom) translate along + by 0.10m during steps 1-67 (~ 6.7 = 0.015 m/s), after which they remain stationary.
A.5 MARKOVIAN ENVIRONMENTS
A.5.1 TASK DESCRIPTION
There are Mon-Markovian tasks: Button press on/off and Button press color change. In simu- lation, the workspace is a 80x80em tabletop on top of which sits modular rail that forms a closed square loop along the perimeter. The LeRobot SO10 is mounted on carriage that follows the rail. During an episode, the carriage moves clockwise or counter-clockwise to bring the arm into reach of different workspace.
(1) Button Press On/Off: Four push-buttone are placed 20cm away from each table corners; each has an LED ring with one of red, green. blue, yellow. each episode start, colors are randomly reassigned to corners; all LEDs begin with the ON state. The robot must press buttons in the fixed color order red - green - blue - yellow, independent of location. pressed button turns to the OFF state only while pressed and returns to the ON state on release. This causes the scene to look identical after a correct button press, rendering progress unobservable from single frame (non-Markon noon-Markovian). Success is defined by pressing all four colors in order within the horizon.
(2) Button Press Color Change. Same layout as the previous task, but each button cycles through five states every press: yellow - red - green + blue -> off. The four buttons start with distinct initial colors. The task is to press buttons in the order of their initial colors (red, then green, then blue, then yellow). Thus multiple buttons may display the same color during execution (e.g. after pressing the green button, there will be two blue buttons), and identical frames can correspond to different stages, again non-Markovian. Success is recorded when the all the buttons have been pressed within the horizon.
A.5.2 DATASET
We collect 150 demonstrations per task. Each demonstration contains synchronized RGB images, depth maps, and colorized point clouds, along with robot proprioception and end-effector (EE) sig- nals. For each demonstration, we record approximately 400-500 timesteps. Point clouds are first cropped to the workspace and then downsampled to 1,024 points. Robot proprioception includes joint positions of robot, base position relative to track and base rotation relative mation includes EE position, EE orientation, and gripper open/closed state, We include videos of several demonstrations in the supplementary files.
to track. EE infor-
A.5.3 IMPLEMENTATION DETAILS
Inputs categorized into (1) observations and (2) robot state. For DP3 baseline, we use down- sampled point cloud as observation and for Quest, we use RGB images. Robot state, used as an additional conditioning input, include joint states, base position and rotation relative track, EE po- sition, EE orientation, and gripper open/closed state. The model outputs an action at each timestep consisting of the target EE position, EE orientation, and gripper command.
B ABLATION STUDIES AND HYPERPARAMETER EXPERIMENTS
We conducted seven ablation studies, three of which focused on MGP-Shor and four on MGP-Long. For MGP-Short, the studies included: (1) the refinement step size; (2) the tokenizer's 's codebook size; and (3) the discretization granularity. For MGP-LLong, the studies included: (1) the refinement step
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Published as a conference paper at ICLR 2026
Table 9: Success rates MGP-Short under different refinement steps across Hard tasks.
Hard (5)
steps
Assembly
Hand insert
Pick out of hole
Pick place
Push
Average
step=1
1.00
0.15
0.25
0.20 0.30
0.38 0.53
0.396
step=2
1.00
0.29
0.58
0.540
Table 10: Success rates of MGP-Short under different tokenizer's codebook size across Sora-World Very Hard tasks.
Very Hard (S)
Codebook Size
Shelf place
Disassemble
Stick pull
Stick push
Pick place wall
Average
512
0.23
0.81
0.41
0.87
0.35
0.534
1024
0.20
0.74
0.50
0.90
0.35
0.538
2048
0.21
0.80
0.39
0.85
0.36
0.522
size under challenging conditions; (2) the mask ratio; (3) the scoring strategy; and (4) different execution step sizes.
B. REFINE STEP FOR MGP-SHORT
iterations (1,2,3) for MGP-Short five World yields a success gain of 14.3%, proving the effectiveness of score-based masking scheme, whereas provides statistically significant additional improvement, while incurring higher inference cost. We therefore adopt the default for short-timestep tasks, striking favorable accuracy-latency trade-off.
We ablate the number of
Hard tasks. As shown in Tab. 9 increasing from r 1
B.2 CODEBOOK SIZE FOR MGP-SHORT
We explore the influence of codebook size on five Meta- World Very Hard tasks, training separate models with codebook sizes of 512, 1024, and 2048. The average success rates are 0.534, 0.538, and 0.522, respectively. The gaps are small (1.6%) and show no consistent trend, indicating minimal sensitivity codebook size. Detailed results are shown in Tab. 10.
B.3 DISCRETIZATION GRANULARITY FOR MGP-SHORT
We test with 2 actions/token and 8 actions/token on the five Meta-Woo World Very Hard tasks. Averaged over tasks, actions/token achieves the highest success rate (0.538), outperforming actions/token (0.526, + +1.2%) and 8 actions/token (0.514, +2.4%). The gaps are modest, indicating limited sensi- tivity to granularity. Detailed results are shown in Tab.
B.4 REFINE STEP FOR MGP-LONG UNDER CHALLENGING CONDITIONS
We evaluated E (,, on five dynamic tasks of MGP-Long. Increasing the refinement steps from to r 2 raises the success rate by +5.2%, indicating that the score-based masking scheme is helpful in more challenging settings. Increasing further from 2 to yields < 1% improvement, while incurring higher inference cost. We therefore set 2 for MGP-Long in the main experiments. Detailed results are shown in Tab.
B.5 MASK RATIO FOR MGP-LONG
We vary what proportion of low confidence tokens are resampled. When replanning we rank the unexecuted tokens by posterior confidence and in standard setting mask the bottom 70% for one refinement pass. Changing between 50%, 70%, and 85% shows that 70% yields the best average success across five Meta-Words Very Hard tasks. A 50% ratio underperforms because many low-
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Published as a conference paper at ICLR 2026
Table 11: Success rates of MGP-Short under different Discretization granularity across Meta-Woria Very Hard tasks.
Very Hard
Method
Shelf place
Disassemble
Stick pull
Stick push
Pick place wall
Average
actions/token
0.21
0.90
0.33
0.31
0.526
actions/token
0.74
0.50
0.35
0.538
actions/token
0.25
0.78
0.35
0.86
0.33
0.514
Table 12: Success rates of MGP-Long under different refine step across dynamic environments.
Dynamic Environment (5)
Method
Baskctball
Pick place wall(W)
Pick place wall(T)
Push
Push wall(T)
Average
1.00
0.31
0.15
0.38
0.384
2
1.00
0.30
0.13
0.25
0.50
0.436
1.00
0.31
0.10
0.20
0.48
0.418
confidence tokens remain unedited and can also interfere with accurate regeneration of the masked ones; 85% is comparable to 70% on average. Detailed results are shown in Tab. [3]
B.6 SCORE POLICY FOR MGP-LONG
Token selection for re- re-masking is critical in MGP-Long it determines how effectively high- confidence tokens are reused and focuses computation on truly uncertain positions for targeted finement. We ablate three confidence-upel update schemes, applied at each replanning step t, to choose which future tokens to re-mask first prior refinement (1) Random: Use random scores mask the rest of the tokens. (2) Score Reuse: Reuse the previous confidences 8:-1 from last steps to select the lowest-confidence tokens. (3) ATR: Recompute current confidences s; from the latest observations using the masked generative transformer (via cross attention) first, then select only the presently low-confidence tokens. We evaluate on ten Meta-Worla Hard and Very Hard tasks, reporting the average success rate. As shown in Tab. [4 ATR scheme attains the highest success, increasing by 10.68% over Random scores and improving by 5.53% from Score Reuse. Score reuse scheme underperforms because its confidences become stale and miscalibrated after environment changes, whereas random score performs worst because it ignores uncertainty and observation cues, re-masking tokens arbitrarily. We therefore adopt ATR scheme before refinement as the default scoring policy for MGP-Long.
B.7 VARYING EXECUTION STEP
On five hard Meta-World tasks, evaluate MGP-Long across execution step sizes of 4, 12, and 36. A step size of 12 yields the highest success (54%), while steps of 4 and 36 achieve 47.8% and 48% respectively, and all settings outperform MGP-Short. Detailed results are shown Tab, 15
C ADDITIONAL SIMULATION RESULTS
C.1 EVALUATION STANDARD BENCHMARKS
C.1.1 SINGLE TASK TRAINING OF METAWORLD
We evaluate MGP-Short on 50 tasks in SooadWorld benchmark across different difficult levels.
Detailed results shown in Tab. [6
We further evaluate MGP-Long on ten Hard and Very Hard tasks. Detailed results are shown in
Tab. 17
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Published as a conference paper at ICLR 2026
Table 13: Success rates of MGP-Long under different mask ratios across
Very Hard
tasks.
Very Hard (5)
Mask Ratio
Shelf place
Disasscmble
Stick pull
Stick push
Pick place wall
Average
50%
0.25
0.83
0.41
1.00
0.31
0,560
70%
0.29
0.86
0.45
1.00
0.33
0,586
85%
0.27
0.85
0.45
1.00
0.31
0.576
Table Success rates MGP-Long under different scoring policies across Meta- Meta-World Hard and Very Hard tasks.
Hard
Method
Assembly
insert
out hole
Pick place
Push
Average
MGP-Long w.rs
0.16
0.42 0,47
0.12
0.47
0.434
MGP-Long w.ls
1.00
0.22
0.15
0.50
0,468
MGP-Long w.ns (ours)
1.00
0.29
0,58
0,30
0,53
0,540
Very Hard (5)
Method
Shelf place
Disassemble
Stick pull
Stick Push
Pick place Wall
Average
MGP-Long
0.11
0.75
0.32
0.95
0.26
0.478
MGP-Long wlss
0.25
0.76
0.40
1.00
0,32
0.546
MGP-Long wans (ours)
0.29
0.86
0.45
1.00
0.33
0,586
C.1.2 MULTITASK TRAINING OF LIBERO90
We evaluate MGP-Short across all 90 tasks of LIBERO-90 multi-task training setting, Detailed results are shown in Tab, [8
C.1.3 MULTITASK TRAINING OF LIBERO10
In the LIBERO-10 benchmark, the task IDs correspond follows:
pick up the book and place in the back compartment of the caddy; Task 2 (LIVING ROOM SCENE6) put the white mug the plate and then place the chocolate pudding to the right of the plate;
Task 1
Task 3 REVING_ROOM, ROOM_SCENEE) put the white mug the left plate and the yellow-and-white mug on the right plate;
Task 4 (LIVING.ROOM ROOM SCENE2) put both the cream cheese box and the butter in the basket;
Task 5 (LIVING,ROOM,SCENN)
put both the alphabet soup and the tomato sauce the basket;
Task 6 (LIVING..ROM..CENN)
place both the alphabet soup and the cream cheese box in the
basket;
Task 7 (KITCHEN,SCEN)) put both moka pots on the stove;
Task 8 (KITCHEN SCENE6) put the yellow-and-white mug in the microwave and close it;
Task 9 (KITCHEN SCENE4) put the black bowl in the bottom drawer of the cabinet and close it;
Task 10 (KITCHEN
turn on the stove and put the moka pot on it.
Detailed results shown in Tab. 19
Table 19: Success Rate of Multitask training on Libero- 10.
Task ID
3
4
7
10
Average SR
MGP-Short (ours)
0.78
0.66
0.60
0.86
0.48
0.92
0.78
0.78
0.92
0.92
0.770
MGP-Long (ours)
0.70
0.73
0.90
0.55
0.94
0.86
0.83
0.95
0.93
0.820
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Published as a conference paper at ICLR 2026
Table 15:
Success rates of MGP-Long under different execution steps across Hard
tasks.
Hard (5)
Method
Assembly
Hand insert
Pick hole
Pick place
Push
Average
step=4
1.00
0.27
0.50
0.25
0.37
0.478
step=12
1.00
0.29
0.58
0.30
0.53
0.540
step=36
1.00
0.24
0.48
0.25
0.43
0.480
Table 16: Success rates of Diffusion Policy, 3D Diffusion Policy and MGP-Short (ours) across all tasks of Meta-World.
Task
Easy
Rutton
Button Press Toplown
Topdowe
Press
DP
0.99
0.98
0.96
0.97
0.99
0.53
DP3
L,00
1.00
0,99
0.99
1.00
0.66
MGP-Shorl (ours)
1,00
1.00
1,00
1,00
1.00
0.65
Taskk
Easy (28)
Duur
open
upen
0.98
DPP3
L,00
0.98
0.99
1,00
1.00
1.00
MGP-Short (ours)
LJ0I
1.00
1.00
1.000
1.00
1.00
TaskkTask
Easy (28)
Faueet close
Faucet open
Handle press
Hundle pull
Handle press
Handle side
L.0I
0.27
1.000
0.53
MGP-Shor Short (ours)
1.00
1.00
1.00
0.65
1.00
0.85
Alg& Task
Tla&/Task
Easy (28)
lever pull
plate slide
plate slide back
plate slide back sidee
plate slide side
Resch wall
DP
0.49
0.83
0:99
1,00
1.00
0.59
(ours)
AlgS/Task
Easy (28)
window close
window open
peg unplag säde
reach
Average
DP
LJKI
1.00
0:74
0.118
0,836
DP3
1,00
1.00
0.24
1.00
0.44
Alg&TTak
Mediuu (11)
Baskethall
Bin picking
Box close
Coffee pull
Coffee Pash
Hammer
DP
0.85
0.15
0.30
0.34
0.67
0.15
DP3
0.98
0.34
0:42
0,88
0.94
0.76
MGP Short (ours)
1,00
0.24
0.57
0,87
0.89
Alg&TTask Task
Medium
Peg insert
sweep
sweep
Average
DP
0.34
0.20
0.14
0.18
0.311
DP3
1.69
0.49
D.18
0.96
0.15
0.616
MGP-Short (ours)
0.49
0,77
0.40
0.70
0,39
(.650
Task
Hard (5)
Assembly
Hand
Pick
Pick place
Average
0.15
0.00
10108
DP3
0.99
0.14
0.14
0.12
0.51
0.380
MGP (ours)
L.00
0.19
0.15
0.35
0.52
0.440
Alg&TTsk
Very Hard (5)
Sheld
Disassemble
Stick pull
Stick Push
Pick place Wall
Average
0.97
MGP-SShort (ours)
0.2
0.74
0.50
0.90
0.35
0.538
C.2 EVALUATION OBSERVATION- -INSSIGINN-MMIIING ENVIRONMENTS
We evaluate MGP-Short and MGP-Long in observation missing environments. Detailed results are shown in Tab. 20
From the results, we can see as the fully. With p=0.35/0.50/0.70 (where p is the probability that missing observations occur for each rollout), MGP-Long achieves 0.484/0.466/0.462 success rate on Hard and 0.564/0.550/0.566 suc- cess rate Very Hard. Overall, the performance remains stable when observations missing
rate increases, performance degrades grace-
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Published as a conference paper at ICLR 2026
Table 17: Success rates of 3D Diffusion Policy, MGP-Short (ours), Full sequence DP3, Full quence MGP, MGP without scored-base base mask and MGP-Long (ours) across Hard and Very Hard tasks of Meta-Wo dera-World.
Hard (5)
Alg& Task
Assembly
Hand insert
Pick out of hole
Pick place
Push
Average
DP3
0.99
0.14
0.14
0.12
0.51
0.380
MGP-Short (ours)
1.00
0.19
0.15
0.35
0.52
0.440
DP3-Full Seq.
0.20
0.17
0.25
0.12
0.20
0.188
MGP-Full Seq.
0.30
0.18
0,50
0.14
0.35
0.294
MGP-w/o SM
1.00
0.27
0.49
0,30
0.49
0.510
MGP-Long (ours)
1.00
0.29
0.58
0.30
0.53
0.540
Very Hard (5)
Alg&Tast
Shelf place
Disassemble
Stick pull
Stick Push
Pick place Wall
Average
DP3
0.17
0.69
0.27
0.97
0.35
0.490
MGP-Short (ours)
0.20
0.74
0.50
0.90
0.35
0,538
DP3-Full Seq.
0.15
0.63
0.10
0.67
0.20
0,350
MGP-Full Seq.
0.19
0.75
0.12
0.72
0.15
0,386
MGP-w/o SM
0.28
0.83
0.44
1.00
0.31
0,572
MGP-LLong (ours)
0.29
0.86
0.45
1.00
0.33
0.586
most of the time and consistently surpasses short-horizon baselines. Even at p=0.70, MGP-Long exceeds MGP-Short by 30.9% and DP3 by 31.5% demonstrating strong robustness to high missing- observation rates.
C.3 EVALUATION ON DYNAMIC ENVIRONMENTS
Qualitative results of MGP-Long shown in Fig. The figure covers five dynamic scenarios:(a) a moving target in Push (Wall); (b) moving goal location in Pick-Place (Wall); (c) a moving target in Push; (d) moving hoop/net in the Basketball environment and (e) moving wall (obstacle) in Pick-Place (Wall). We provide videos visualizing the results of all dynamic environments in the supplementary material.
Analysis of failure cases. In one-shot methods (DP3-Full MGP-Full), the plan is fixed at a 0, i.c. there are no closed-loop updates: thus, when the world changes, the planned action trajectory becomes stale, leading to missed contacts, wrong targets, and compounding errors. Short-horizon DP3, with its tiny context window, overly reactive: it lags moving objects, overfits local noise, and can oscillate when the task needs longer-range context memory. For MGP-Long (ours), most failures arise when changes outpace the replan cadence or the required edits exceed the current mask window; perception latency occlusion also delay corrections. For example, in dynamic Push with a moving goal, the robot briefly chases the old position and zigzags until confidence drops and that segment rewritten.
C.4 EVALUATION MON-MARKOVIAN ENVIRONMENTS
Qualitative results of MGP-Long and short-horizon baselines of Button Press On/Off and Button Press Change Color task are shown in Fig. 5 and Fig. 6 respectively. From the qualitative results, we can see that MGP-Long can press the buttons with different colors the right order. However, short-horizon baselines often stall or press the wrong buttons. We provide videos visualizing the results of MGP-Long and short-horizon baselines across all Non-Markovian environments in the supplementary material.
Analysis of failure cases. The failures come from non-Markovian aliasing and myopic control: once the brief color cue disappears, subsequent observations longer reveal the latent goal, and policies that rely on short temporal context window (without long timestep memory or explicit plan) either hesitate near the panel guess plausible- but eecorrettsequenee A single mis ppees then changes the hidden state; without a mechanism to replan, the controller falls into press uuppess oscillations repeats the same action.
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Table 18: Success Rate and Inference Time of Multitask training on Libero-90. Unit of inference time is ms/step.
Success Rate
Task ID
ResNet-T
ACT
PRISE
VQ-BeT
Diffusion Policy
Quest
MGP-Short (ours)
1.00
0.90
0.80
1.00
0.99
1.00
0.97
0.96
0.30
0.35
0.94
0.98
0,97
0.97
3
0.96
0,50
0.70
0.97
0.99
0.91
0.97
4
0.74
0.22
0.50
0.99
0.91
0.94
0.77
5
0.95
0.58
0.45
0.95
0,93
0,98
0.97
6
0.91
0.39
0.65
0.98
0.99
0.98
0.97
7
0.95
0.29
0.50
0.86
0.94
0.93
0.87
8
0.96
0.72
0.95
0.80
0.90
0.99
0.90
9
0.74
0.41
0.93
0.60
0.75
0.93
0.87
10
0.97
0.65
0.35
0.83
0.91
0,90
1.00
11
0.97
0.82
0.95
0.96
0.98
0.97
1,00
12
0.90
0.73
0.95
0.80
0.94
0.94
0.93
13
0.82
0.62
0.20
0.87
0.81
0.76
0.87
14
0.86
0.72
0.40
0.49
0.94
0.71
0.80
15
0.87
0.49
0.35
0.46
0.95
0.58
0.64
16
0.97
0.86
0.75
0.98
0.99
0.96
0.93
17
0.72
0.40
0.40
0.53
0.89
0.80
0.77
18
0.79
0.20
0.15
0.80
0.76
0.67
0.67
19
0.83
0.75
0.30
0.91
0.99
1.00
0.90
20
0.87
0.41
0.65
0.68
0.99
1.00
0.90
21
1.00
0.82
1.00
0.96
0.99
1.00
1.00
22
0.99
0.44
0.30
0.91
0.97
0.93
0.97
23
0.97
0.75
0.85
0.95
0.99
0.92
0.97
24
0.75
0.90
0.80
1.00
0.99
1.00
0.90
25
0.97
0.44
0.95
0.94
0.99
1.00
1.00
26
0.97
0.85
0.90
0.85
1.00
0.99
0.97
27
0.72
0.14
0.55
0.50
0.88
0.52
0.70
28
0.72
0.20
0.05
0.45
0.86
0.68
0.60
29
1.00
0.68
1.00
0.97
1.00
1.00
1.00
30
1.00
0.19
1.00
0.92
1.00
0.97
1.00
0.91
0.83
0.50
0.85
0.96
0.90
0.97
32
0.99
0.90
0.85
0.88
1.00
0.99
1.00
33
0.57
0.20
0.20
0.37
0.58
0.67
0.67
34
0.85
0.56
0.30
0.87
0.84
0.98
0.97
35
0.93
0.52
0.80
0.98
0.97
0.92
0.90
36
0.97
0.67
0.75
0.98
0.99
0.97
0.97
37
0,85
0.24
0.25
0.73
0.97
0.74
0.80
38
0.78
0.41
0.30
0.90
0.91
0,62
0.67
39
0.86
0.32
0.20
0.90
0.90
0.88
0.90
40
0.96
0.35
0.85
0.90
0.98
0.93
0.93
41
0.90
0.27
0.50
0.91
0.79
0.92
0.93
42
1.00
0.74
0.55
0.89
1.00
1.00
1.00
43
0.98
0.41
0.80
0.97
0.99
0.98
0.97
44
0.80
0.39
0.40
0.83
0.89
0.93
0.87
45
0.99
0.83
0.85
0.99
1.00
0.98
0.93
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Published as a conference paper at ICLR 2026
Success Rate
Task ID
ResNet-T
ACT
PRISE
VQ-BeT
Diffusion Policy
Quest
MGP (ours)
46
0.97
0.60
0.55
0.91
1.00
0.99
1,00
47
0.75
0.37
0.35
0.65
0.31
0.91
0.83
48
0.87
0.27
0.25
0.88
0.53
0.98
1.00
49
0.90
0.55
0.65
0.48
0.96
0.95
0,90
50
0.88
0.54
0.65
0.60
0.82
0.99
0.78
51
0.80
0.33
0.40
0.87
0.28
0.88
0.70
52
0.79
0.28
0.10
0.91
0.00
0.75
0.70
53
0.74
0.33
0.30
0.84
0.34
0.82
0.83
54
0.88
0.64
0.60
0.79
0.73
0.87
0.83
55
0.83
0.51
0.50
0.95
0.77
0.93
0,97
56
0.85
0.62
0.35
0.92
0.49
0.83
0.93
57
0.99
0.64
0,80
1.00
1.00
0.97
0.93
58
0.95
0.57
0.50
1.00
1.00
0.99
1.00
59
0.84
0.56
0.20
0.98
0.78
0.95
0.87
60
0.94
0.68
0.65
0.91
0.89
1.00
0.97
61
0.91
0.95
0.80
0.98
0.90
1.00
1.00
62
0.96
0.75
0.85
0.99
0.58
0.81
0.97
63
0.70
0.43
0.40
0.84
0.38
0.78
0,90
64
0.73
0.04
0.40
0.38
0.41
0.78
0.70
65
0.73
0.16
0.15
0.68
0.75
0.85
0.90
66
0.76
0.45
0.15
0.84
0.65
0.79
0.77
67
0.84
0.72
0.30
0.87
0.66
0.93
0,90
68
0.78
0.73
0.55
0.74
0.44
0.85
0.93
69
0.83
0.68
0.85
0.90
0.59
0.93
0.90
70
0.88
0.56
0.90
0.93
0.57
0.89
0.93
71
0.90
0.52
0.55
0.97
0.92
0.92
0.97
72
0.85
0.52
0.35
0.85
0.98
0.94
0.87
73
0.89
0.59
0.60
0.84
0.86
0.98
0.77
74
0.72
0.18
0.30
0.33
0.61
0.70
0.77
75
0.77
0.45
0,45
0.95
0.38
0.95
1.00
76
0.64
0.22
0.25
0.30
0.21
0.61
0.73
77
0.89
0.70
0.65
0.70
0.35
0.89
0.87
78
0.57
0.46
0.80
0.85
0.14
0.97
0.97
79
0.63
0.28
0.45
0.68
0.06
0.86
0.84
80
0.73
0.59
0.30
0.87
0.01
0.98
1.00
81
0.65
0.53
0.30
0.44
0.08
0.70
0.80
82
0.63
0.24
0.35
0.61
0.54
0.70
0.73
83
0.80
0.56
0.80
0.89
0.49
0.94
0,97
84
0.55
0.35
0.55
0.43
0.47
0.75
0.87
85
0.70
0.74
0.75
0.93
0.79
0.92
1.00
86
0.69
0.53
0.75
0.47
0.13
0.89
1.00
87
0.84
0.65
0.95
0.86
0.98
0.92
0.87
88
0.82
0.54
0.65
0.87
0.96
0.97
1.00
89
0.91
0.77
0.55
0.96
0.70
0.97
0.90
90
0.80
0.29
0.85
0.89
0.91
0.56
0.70
25

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Published as a conference paper at ICLR 2026
Table 20: Success rates of 3D Diffusion Policy, MGP-Short (ours), Full sequence DP3, Full se- quence MGP, MGP without scored- based mask and MGP-Long (ours) across Hard and Very Hard tasks of Meta-World under Observation-missing environments.
Hard (5)
Alg&Task
Assembly
Hand
Pick of hole
Pick place
Push
Average
DP3 (p=0.35)
0.16
0.05
0.20
0.24
0.160
MGP-Short (ours) (p=0.35)
0.15
0.20
0.05
0.17
0.29
0.172
DP3-Full Seq.
0.20
0.17
0.25
0.12
0.20
0.188
MGP-Full Seq.
0.30
0.18
0.50
0,14
0.35
0.294
MGP-w/o SM (p=0.35)
0.48
0.17
0.25
0,416
MGP-Long (0035)p=0.35)
1.00
0.23
0.54
0.20
0,45
0.484
MGP-Long (ours)(==0.50)
1.00
0.20
0.53
0.20
0.40
0.466
MGP-Long (pu.5))==0.70)
1.00
0.18
0.57
0.18
0.38
0.462
Very Hard (5)
Alg&Task
Shelf place
Disasseble
Stick pull
Stick Push
Pick place Wall
Average
DP3
0.12
0.30
0.18
0.37
0.23
0.240
MGP-Short (ours)
0.15
0.27
0.16
0.41
0.20
0.238
DP3-Full Seq.
0.15
0.63
0.10)
0.67
0..0
0.350
MGP-Full Seq.
0.12
0.15
MGP-w/o SM(p=0.35)
0.20
0.84
0.39
0.96
0.30
0.538
MGP--Lng (0.35)p=0.35)
0.20
0.87
0,43
1.00
0,32
0.564
MGP-Long (ours)(==0.50)
0.22
0.82
0.41
1.00
0.30
0.550
MGP-Long (ours)(p=0.70)
0.86
0.42
1.00
0.31
0.566
Task Progress in Dynamic Environment
(a)
(b)
(d)
Figure 4: Qualitative results Dynamic environments
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Published as a conference paper at ICLR 2026
Task Progress in Non-Markovian Environment (Button-On/Off)
(al
Fallure Gases for Short-horizon Besalings
(b)
Figure 5: Qualitative results of Button Press On/Off Tasks. (a) is the result of MGP-Long: (b) and (c) are results of Short-Horizon baselines.
Progress Environment (Button-Change Color)
(al
Fallure
Figure 6: Qualitative results Button Press Color Change Tasks. (a) is the result MGP-Long; (b) and (c) results of Short-Horizon baselines.
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Published as a conference paper at ICLR 2026
C.5 CONFIDENCE SCORE ANALYSIS
To access how the confidence score behaves in practice, we plot token-level confidence refine- ment passes as MGP-Long executes in Meta-Worle and in a dynamic environment.
Fig.
(quasi-static, 'Very Hard'), ) Fig. (b) shows the corresponding mask-unmask map (black = masked at the first refinement,
(a) shows confidence heatmap for
prefix). The horizontal axis corresponds to steps at which replanning occurs; after each step, 12 actions, corresponding to three tokens, are executed before replanning again. Two refinement passes occur at each replanning step. The vertical axis indexes temporal action tokens. Fig. ([c) shows the rollout, with frequently edited action segments highlighted in red. The model is confident during approach motions but its confidence declines at grasp-and-liftt particularly when the first grasp fails and requires multiple corrections.
gray masked the second; the pink band marks the already-execuu
In order to investigate confidence under distribution shift, we also visualize how the confidence score behaves when the environment changes mid-episode. We consider a dynamic basketball task in which the scene is static at the start, and the hoop begins moving continuously halfway through rollout. Fig- a)) shows the per-token confidence heatmap; Fig. B(b) shows the corresponding mask-unmass map; Fig. [8cc illustrates the rollout, with actions frequently edited highlighted in red; these coincide with the onset of hoop motion. Across these views, confidence drops and masking clusters around the transition to motion, which indicates that the policy focuses edits exactly where
the dynamics shift.
We also measure the fraction of tigh-confidence low-confidence tokens that the model updates with a different value if prompted to resample them, as well as the impact of resampling high instead of low confidence tokens. On MetaWorld-DDssssemble at each replan we either (i) mask the bottom 70% (default) or (ii) mask the top 70% high-confidence tokens, and report task success and the flip rate (fraction of masked tokens that change after refinement). With low-confidence masking the policy reaches 0.86 success with a 60.6% flip rate-the model frequently edits tokens it was least confident about. With high-confidence masking, the flip rate falls 15. %, showing the model mostly keeps high-confidence tokens unchanged even when forced to revisit them. This indicates the confidence scores are probabilistically well calibrated.
C.6 MODEL SIZE AND SPEED
Although training proceeds in two stages, the system is lightweight in practice: our policy has 7M parameters vs. ~262M for DP3 ("37x fewer), and training for 2000 epochs takes ~55 minutes vs. ~3.3 hours under the same setup. Inference is also lighter: diffusion policies require many iterative denoising steps, whereas masked-generatinn planner predicts in parallel and performs only one or two refinement passes, yielding substantially lower deployment latency. Thus, the two-stage design does increase runtime complexity and, in our experiments, is faster train and deploy.
C.7 ACCURACY OF TOKENIZED ACTIONS
To assess any inaccuracies due to mismatch between discrete tokens and continuous actions, we directly measured VQ-VAE reconstruction quality. On Socaaorrd-Diissemblle the tokenizer's average per-step L2 reconstruction error is structed actions are indistinguishable. Moreover, replacing ground- -ruth actions with their VQ-VAE reconstructions and replaying them yields 100% task success MetaWorld. These results indicate that quantization error is negligible for control in our setting.
10-4, visual overlays of ground-truth and recon-
C.8 MULTIMODALITY ANALYSIS
The Gumbel-Max trick ensures that token sampling still follows the logits' softmax probability dis- tribution, preserving diversity in the sampled results. To verify this, we conducted a qualitative evaluation of model on the Push-T task Florence et al. (202I) In the initial stage, the control point, T-block, and target are placed symmetrically in fixed locations. MGP-Shor then autore- gressively generates 16 subsequent actions (4 tokens) and executes the first 8 actions based the
28

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Published as a conference paper at ICLR 2026
Iteraiion (steas)
Recotion (5teps
10
14
50-
180
40
30
30
Token trdex
20
10
(a)
(b)
(c)
Figure 7: Confidence score in MetaWorld 'Disassemble' environment. (a) Per-token confidence heatmap across refinement iterations. (b) Mask-unmask map (black masked at first refinement; gray masked second; pink executed prefix). (c) Rollout snapshots with frequently edited seg- ments highlighted. Confidence remains high during approach motions, then drops at fine, outcome- critical manipulations when the gripper must accurately grasp and lift the ring. These low-confidence tokens are repeatedly masked and corrected, showing that refinement concentrates edits where they matter.
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Iteration (Steps]
Istepal
10 31 12
14 15 16
45
0.
20
Tolenn indexx
Canboree INto
-099
20
30
(a)
(b)
(c)
Figure 8: Confidence under distribution shift in Dynamic 'Basketball' environment. (a) Per- token confidence heatmap across refinement iterations. (b) Mask-unmask map (black masked at first refinement; gray masked second; pink executed prefix). (c) Rollout snapshots with fre- quently edited action segments highlighted. The hoop begins moving midway through the episode; confidence drops and masks cluster around the onset of motion, indicating that edits occur precisely when the dynamics change.
30

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Published as a conference paper at ICLR 2026
Figure 9: 1: The control point trajectories of the first 40 frames from 20 successful episodes. 2. An example pushing the T-block from above. 3. An example of pushing the T-block from below.
8
888
bb)
Figure 10: Qualitative results of MGP-Long towel-sorting tasks. (a) is the results when the initial light red; (b) is the results when the initial light is blue. (c) Failure case of DP3-Full Seq. on towel-sorting tasks.
previous frame's ' image and the control point position. The resulting trajectory plots (Fig. show that MGP's sampling still preserves the diversity distribution of the original data.
D REAL-WORLD EXPERIMENTS
D.1 IMPLEMENTATION DETAILS
We collect 60 human demonstrations. Each demonstration contains synchronized RGB images, depth maps, and colored point clouds, along with robot proprioception and end-effector (EE) signals. For each demonstration, the workspace and then downsampled to 1,024 points. Inputs are categorized into (1) observations and (2) robot state. We use downsampled point cloud observations. Robot state, used additional conditioning input, includes joint states, EE position, EE orientation, and gripper state. The model outputs action at each timestep consisting of the target EE position, EE orientation, and gripper state.
record approximately 100 timesteps. Point clouds are first cropped to
D.2 QUALITATIVE RESULTS
The qualitative results of MGP-Long on the towel-sorting task with different initial light colors are shown in Fig. (a) and (b). We can clearly see that, even though the light switches off immediately after turning on, the robot still places the towel into the correct basket. contrast, short-horizon baselines fail, they cannot infer the correct basket from the available observations. Fig. 10 (c) also illustrates a typical failure case of DP3-FullSeq, where the robot fails to pick up the towel at the start.
We further investigate which inputs are most critical for performance. We find that the end-effector position is important, while the joint states have comparatively less influence. Visual observations (point clouds) play major role: when we introduce light color not seen in the training data (c.g., yellow), the robot still executes plausible motion but fails to grasp the towel (it assumes the towel
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Published as a conference paper at ICLR 2026
is further to the left) and attempts to place it in the left basket. When completely remove the point-cloud input, the policy becomes confused and simply hovers above the towel without taking action.
E
USE OF LARGE LANGUAGE MODELS (LLMS)
Portions of the writing in this paper were polished using large language models.
32


Reviews:
[{"review_id": "JLM2dqBtYF", "reviewer": "Reviewer_x3HB", "summary": "This paper introduces the Masked Generative Policy (MGP), a novel framework for robot imitation learning that eliminates the inference bottlenecks of diffusion models and the sequential constraints of autoregressive models.\nMGP-Short is specifically designed for Markovian tasks, adapting the masked generative transformer for short-horizon sampling. It demonstrates improved success rates on standard benchmarks while significantly reducing inference time.\nMGP-Long allows for globally coherent predictions over long horizons, enabling dynamic adaptation, robust execution under partial observability, and efficient, flexible execution. It achieves state-of-the-art results in dynamic, observation-missing, and non-Markovian long-duration environments.\nThe authors validated the effectiveness of MGP in multiple simulated environments.", "questions": "1. The results in the simulated environment are impressive. However, due to the sim-to-real gap, it is often necessary to demonstrate effectiveness in real-world settings within this field. \n2. How can we ensure the accuracy of the confidence score itself?\n3. Regarding the MGP-Long settings: In lengthy sequences, some objects may lead to environmental changes as a result of prior actions. When this occurs, the predictions may lose their overall coherence, necessitating the generation of a new action sequence that takes into account the modified objects.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The authors conducted a thorough analysis of current action generation methods and proposed MGP to address the latency issues inherent in diffusion-style or autoregressive-style action generation. The paper is clearly articulated and easy to follow. The concept of using MGP to re-predict tokens with low confidence while maintaining those with high confidence is intriguing. Theoretically, this approach could indeed reduce the time consumed in predicting actions.", "weaknesses": "1. I acknowledge that the results in the simulated environment are impressive. However, due to the sim-to-real gap, it is often necessary to demonstrate effectiveness in real-world settings within this field.\n\n2. Regarding the confidence score. Could you analyze the situations that might lead to a lower confidence score? Additionally, how can we ensure the accuracy of the confidence score itself?\n\n3. About the MGP-Long settings. In long sequences, certain objects may cause environmental changes due to previous actions. At this point, the predictions may no longer remain globally coherent, and we would need to generate a new action sequence based on the changed objects.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DXBjFrzljW", "reviewer": "Reviewer_UA2E", "summary": "This manuscript proposes a novel imitation learning framework for learning visuomotor policy parameterized by masked generative transformer (MGT), which enables high inference efficiency for closed-loop control while maintaining robustness in long-horizon and non-Markovian tasks. Specifically, two sampling strategies are designed: (1) MGP-Short performs short-horizon sampling and refines action tokens with few iterations for the best performance-efficiency trade-off in Markovian tasks; and (2) MGP-Long samples the full trajectory and adaptively refines tokens with updated observations from the environment to retain global coherence. Experiments demonstrate the strong performance of the proposed methods in Markovian and more challenging tasks.", "questions": "- Typo: “blcoks” -> “blocks” in line 191\n- In Figure 3, should the unexecuted token “52” at the bottom left be “53” before Posterior-Confidence Estimation\n- In line 269, the authors mentioned four ablation studies were conducted, but in section 4.5, only three of them are elaborated.\n- How many actions are encoded into one discrete token? And would that hyperparameter affect performance on different tasks?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- Unlike diffusion-based policy, which might require external distillation for fast inference speed, MGP puts less stress on iterative sampling for obtaining clean actions, and has high flexibility of test-time adjustment with proposed sampling strategies.\n- MGP-Long iteratively refines the action tokens using the executed actions along with the updated observation to improve trajectory-level coherence, which achieves strong performance in Non-Markovian and dynamic environments, and remains robust to missing observations", "weaknesses": "- Baselines such as diffusion-based policies (e.g. ) as well as VQ-BeT stand out when learning multimodal action distributions, while MGP is also built on top of vector quantization, it is not yet clear how the proposed sampling methods work on tasks with explicit multimodality\n- As all tokens are predicted in parallel, the refinement process can be affected if there are low-quality actions predicted initially with high confidence, causing error accumulation throughout the following iterations. Furthermore, it would be helpful to extend the first ablation studies to investigate how many performance gains can be obtained from more refinement steps, especially in more challenging environments.\n- Please include standard deviations in the table for thoroughness if multiple seeds are used to aggregate the result.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "DGhm307ov9", "reviewer": "Reviewer_4ZDe", "summary": "This paper introduces Masked Generative Policy (MGP), a new visuomotor imitation learning framework that models robot control as a masked token-generation problem.\nMGP first discretizes continuous actions with a VQ-VAE tokenizer, then trains a masked generative transformer (MGT) to reconstruct full action sequences from partially masked tokens conditioned on current observations.\n\nTwo inference paradigms are proposed:\n\n- MGP-Short for Markovian, short-horizon tasks: parallel token generation with one or two score-based refinement steps.\n\n- MGP-Long for non-Markovian, long-horizon tasks: predicts the entire trajectory in one pass and adaptively refines uncertain future tokens through posterior-confidence estimation (PCE) as new observations arrive.\n\nExtensive experiments on Meta-World and LIBERO benchmarks show strong gains—up to 35× faster inference and higher success rates (+9% overall, +60% in dynamic or missing-observation settings).\nAblations (MGP-FullSeq, MGP-w/o-SM) validate that PCE-based selective refinement is critical for efficiency and global coherence.", "questions": "How is the confidence-based masking threshold determined? Fixed ratio or adaptive per step?\n\nDoes the posterior-confidence estimation ever over-mask or destabilize refinement when confidence calibration drifts?\n\nHow sensitive is performance to the tokenizer’s codebook size and discretization granularity?\n\nWould an end-to-end jointly trained transformer + VQ-VAE outperform the current two-stage pipeline?\n\nDiscrete tokens normally introduce information loss—what do the authors believe enables MGP’s discrete representation to outperform continuous-action models like Diffusion Policy? Is it the global trajectory modeling, masked refinement dynamics, or some property of the VQ-VAE discretization?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Original idea: creatively transfers masked-generation paradigms (MaskGIT/MUSE) to robotic action synthesis.\n\nTechnical soundness: clearly defined VQ-VAE tokenizer, transformer conditioning, and confidence-guided refinement loop.\n\nEmpirical rigor: evaluated on 150+ tasks across difficulty levels; includes robustness tests (dynamic, missing-observation, non-Markovian).\n\nFair comparison: benchmarks against continuous-action (diffusion/flow) and discrete-token baselines under identical encoders and demos.\n\nAblation insight: MGP-w/o-SM (without score-based masking) confirms that selective refinement improves both efficiency and success rate.\n\nRelevance: unifies the advantages of diffusion (sample quality) and autoregressive (temporal coherence) methods in a parallelizable design.", "weaknesses": "Limited analysis of tokenizer sensitivity: performance may depend on the VQ-VAE codebook design, but this is not explored.\n\nHyperparameter transparency: the exact confidence-masking threshold and its effect on refinement stability are not analyzed.\n\nPotential complexity: the two-stage training (tokenizer + policy) increases implementation effort; joint end-to-end training would strengthen the approach.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "40tklppfnP", "reviewer": "Reviewer_wnrp", "summary": "The paper introduces Masked Generative Policy, which is a new framework for visuomotor imitation learning that models robot actions as discrete tokens and leverages masked generative transformers to efficiently generate and refine action sequences. Unlike autoregressive or diffusion generative policies, MGP tries to generate globally coherent future plans and refine them online. It combines MaskGIT-style generation with robotic action modeling. The experimental results demonstrate state-of-the-art performance on Markovian and non-Markovian control.", "questions": "- Is the pipeline easy to smoothly transfer to real-world tasks? The robustness to sensor noise, delays, or physical contact uncertainty remains a question, especially when it requires a strong encoder and global context.\n- There were few visual rollouts or per-task failure analyses. How token refinement behaves in specific dynamic scenes could be more illustrative.\n- A discussion section on the potential domain mismatch and increased complexity of the proposed two-stage training is helpful.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- It reframes the policy generation problem as masked generative modeling is new and practical, especially given the latency and horizon challenges in robotics. The tokenization of actions is smart to allow transformer modeling of full sequences.\n- The global coherence maintains long-horizon consistency through token memory. The parallel sampling and selective refinement drastically cut latency, leading to high inference efficiency.\n- The experimental results are comprehensive and demonstrate the effectiveness of the proposed method across simulations and tasks. While diffusion models model smooth distributions and autoregressive models enforce causality, MGP smartly bridges them using mask-and-refine semantics, achieving both speed and robustness.", "weaknesses": "- The system design and two-stage training are complex. The VQ-VAE and MGT pipeline introduces extra overhead and possible distribution shift between discrete tokens and true continuous actions.\n- When predicting all tokens at once, it loses the explicit notion of conditioning the next tokens on the current action. In dynamic control, this can lead to physically inconsistent predictions.\n- The model must have enough context to predict consistent future tokens without sequential conditioning. It could work in structured simulation, but may fail with partial observability or noisy real-world sensors where causality exists.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)