The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the optimization landscape of policy learning. While recent advances have focused heavily on scaling training data and model capacity, the choice of action space remains guided by ad-hoc heuristics or legacy designs, leading to an ambiguous understanding of robotic policy design philosophies. To address this ambiguity, we conducted a large-scale and systematic empirical study, confirming that the action space does have significant and complex impacts on robotic policy learning. We dissect the action design space along temporal and spatial axes, facilitating a structured analysis of how these choices govern both policy learnability and control stability. Based on 13,000+ real-world rollouts on a bimanual robot and evaluation on 500+ trained models over four scenarios, we examine the trade-offs between absolute vs. delta representations, and joint-space vs. task-space parameterizations. Our large-scale results suggest that properly designing the policy to predict delta actions consistently improves performance, while joint-space and task-space representations offer complementary strengths, favoring control stability and generalization, respectively.
Figures & tables
Figure 1: Overview of our study on action space design. (a) Historical analysis shows the divergent usage of action spaces (Absolute vs. Delta, Joint vs. EEF) in existing literature. (b) Our experimental setup includes an action abstraction taxonomy and a large-scale benchmark on both simulation and real-world platforms. We invest over 13,000 real-world rollouts to quantify the impact of these design choices, revealing significant performance gaps and identifying best practices for robotic manipulation under various scenarios.
Figure 2: Hierarchy of the action space for robotic manipulation policies and its abstraction taxonomy
Figure 3: (a) We verified that chunk-wise delta for both EEF and Joint perform better than step-wise delta representations. (b) Grid search over execution horizons across four different action space.
Figure 4
Figure 5: Consistency of Action Space Superiority under Scaling. We evaluate policy performance across varying (a) training epochs and (b) number of demonstrations. The top row illustrates the individual performance of the four action space, while the bottom row aggregates the performance across temporal ( abs vs. delta ) and spatial ( task vs. joint ) dimensions, providing an intuitive comparison.
Figure 6: Comparison across different action space on advanced learning settings
Appendix figures & tables10 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 7
Figure 8: Overview of hardware setup for real world experiments
Task
Goal
Progress Score
Evaluation Focus
Touch Cube
Sanity Check : Move from a random pose to touch a fixed cube.
0.5 for aiming the correct direction; 0.75 for touching the cube corner; 1.0 for touching the top surface.
Isolates spatial precision from dynamics; validates basic convergence.
Pick Cup
Grasping : Grasp and lift a target cup to a height of 10cm .
0.5 for touching the cup; 1.0 for picking up the cup.
Introduces contact dynamics and gripper timing coordination.
Pick & Place
Sequential : Grasp a cup and place it onto a target plate.
0.5 for touching the cup; 0.75 for picking up the cup but failing to put it on the plate; 1.00 for successfully placing it on the plate.
Sensitive to temporal drift and error accumulation over a long horizon.
Bimanual Transfer
Coordination : Transfer a cube from the left arm to a bowl in the right arm.
0.25 for touching either the cube or the bowl; 0.5 for pick up either; 0.75 for picking up both but failing to put the cube into the bowl; 1.0 for aiming correctly.
Requires inter-arm coordination and precise relative positioning.
Appendix
Table 3: Task Curriculum, Complexity Progression, and Success Criteria. We design four tasks with increasing difficulty to stress different failure modes of action representations and define rigorous evaluation metrics for each.
Figure 9: Cross-validation with a flow-matching backbone. Chunk-wise delta representations, in both end-effector and joint space, consistently outperform step-wise delta action interfaces.
Figure 10: Results on RoboTwin 2.0 cross-validate the scaling laws of action abstractions. Comparison of policy performance across (a) training epochs and (b) demonstration scales for regression-based and flow-matching-based backbones. These results confirm that the proposed method scales effectively with both increased compute and data.
Figure 11: Scaling experiments on multi-task learning
Figure 12: Problem reformulation.
Method
Control
# Data
Cube
Pick Cup
Pick and Place
Bimanual Transfer
Average
DP
abs-ee
100
58.70
60.87
69.57
56.52
61.41
rel-ee
80.43
84.78
69.57
45.65
70.11
abs-joint
78.26
60.87
60.87
57.61
64.40
rel-joint
86.96
89.13
73.91
58.70
77.17
abs-ee
250
83.70
65.22
73.91
65.22
72.01
rel-ee
91.30
97.83
84.78
76.09
87.50
Appendix
Table 4: Task success rates under different data regimes
Method
Control
# Epochs
Cube
Pick Cup
Pick and Place
Bimanual Transfer
Average
ACT
abs-ee
300
66.30
52.38
66.30
61.96
61.74
rel-ee
73.91
92.86
91.30
67.39
81.37
abs-joint
65.22
64.29
67.39
51.09
62.00
rel-joint
82.61
95.24
92.39
64.13
83.59
abs-ee
600
77.17
63.04
66.30
63.04
67.39
rel-ee
85.87
97.83
84.78
67.39
83.97
Appendix
Table 5: Task success rates under different training epochs
Task
Method
abs-ee
rel-ee
abs-qpos
rel-qpos
Adjust Bottle
ACT
36.7±3.3
33.3±6.7
23.3±3.3
40.0±0.0
DP
40.0±11.5
30.0±5.8
20.0±5.8
60.0±5.8
Dump Bin Bigbin
ACT
26.7±13.3
50.0±20.0
50.0±5.8
43.3±6.7
DP
16.7±6.7
53.3±8.8
56.7±13.3
53.3±6.7
Grab Roller
ACT
23.3±14.5
40.0±10.0
56.7±14.5
93.3±3.3
DP
30.0±15.3
66.7±18.6
33.3±12.0
80.0±5.8
Appendix
Table 6: Task performance of ACT and DP averaged over three independent trials.
In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we evaluate pose increment, pose velocity, joint position increment, and joint velocity across two vision-based manipulation tasks: object picking and pushing. We train policies in simulation and deploy them to the real world using sim-to-real transfer. We find that action-space representation indeed significantly affects sim-to-real performance. In particular, we find that the joint velocity action space is best for the vision-based picking and pushing tasks in terms of smoothness and final task performance. We also provide practical guidance for RL practitioners in choosing action spaces for both simulation and real-world experiments.
Department of Computing Science, University of Alberta · National Research Council Canada · School of Electrical Engineering and Computer Science, University of Ottawa +3
Learning effective action representations is critical for robotic manipulation, where raw control trajectories are often noisy, redundant, and difficult to model directly. Existing methods mainly encode the structure of the action stream itself, treating the role of actions in the environment as implicit. Yet manipulation is about changing the world: the same action segment can induce different outcomes under different scene contexts, making action semantics inherently environment-dependent. We propose EDAR, an Environment-Dependent Action Representation that grounds action tokens in both executable control structure and expected visual consequences. By coupling motor commands with their environment-conditioned effects, EDAR encourages the learned action space to capture interaction semantics rather than merely command-level patterns. Experiments on simulated and real-robot manipulation benchmarks demonstrate that EDAR improves downstream policy learning, especially in long-horizon manipulation. These results highlight the importance of grounding action representations in executable control structure and environment-conditioned visual change.
Yuecheng Xu, Tong Yang, Jingkai Jia +3
College of Intelligent Robotics and Advanced Manufacturing, Fudan University · Shanghai Key Lab of Intelligent Information Processing, College of Computer Science and Artificial Intelligence, Fudan University · TeleAI, China Telecom
Effectively handling the interplay between spatial perception and action generation remains a critical bottleneck in robotic manipulation. Existing methods typically treat spatial perception and action execution as decoupled or strictly unidirectional processes, fundamentally restricting a robot's ability to master complex manipulation tasks. To address this, we propose X-Imitator, a versatile dual-path framework that models spatial perception and action execution as a tightly coupled bidirectional loop. By reciprocally conditioning current pose predictions on past actions and vice versa, this framework enables continuous mutual refinement between spatial reasoning and action generation. This joint modeling exactly mimics human internal forward models. Designed as a modular architecture, the system can be seamlessly integrated into various visuomotor policies. Extensive experiments across 24 simulated and 3 real-world tasks demonstrate that our framework significantly outperforms both vanilla policies and prior methods utilizing explicit pose guidance. The code will be open sourced.