ReDOA: Real-World Distillation via Observation Alignment for Image-Based Manipulation Policy Learning

Kotaro Kajitsuka; Masaki Murooka; Fumio Kanehiro

cs.ROOct 19, 2026

ReDOA: Real-World Distillation via Observation Alignment for Image-Based Manipulation Policy Learning

Authors: Kotaro Kajitsuka, Masaki Murooka, Fumio Kanehiro

Organizations: University of Tsukuba, Tsukuba, Japan. · CNRS-AIST JRL (Joint Robotics Laboratory), IRL3218, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.

Abstract

Data-driven learning for general-purpose robotic manipulation presents a fundamental trade-off between data-collection cost and adaptability to real-world environments. Imitation learning (IL) requires costly human demonstrations, whereas simulation-based learning is vulnerable to sim-to-real gaps in dynamics and sensor observations. We propose ReDOA (Real-World Distillation via Observation Alignment), a framework that decouples control learning from perception learning to learn image-based policies in the real world. A state-based teacher policy trained in simulation is executed on a real robot, and its control knowledge is distilled into an image-based student policy using the collected trajectories. Observation Alignment resolves observation mismatches between data collection and deployment. This framework collects successful, efficiently executed trajectories paired with real-world visual observations, without requiring demonstrations from human experts. In real-robot experiments on three non-prehensile manipulation tasks, including bimanual manipulation, our method achieves better overall performance when jointly considering task success and execution efficiency than both IL based on human demonstrations and policy distillation in simulation. https://isri-aist.github.io/redoa-project-page/

Figures & tables

Fig. 1: Conceptual overview of ReDOA (Real-World Distillation via Observation Alignment).

Fig. 2: Overview of the ReDOA (Real-World Distillation via Observation Alignment) learning pipeline.

Fig. 3: Workflow for tracker artifact removal from raw demonstration images. We used planar fiducial markers as trackers for this whole study.

Panel of Figure 4 — Fig. 4: Real-world manipulation tasks.

Fig. 5: Comparison of the training pipelines of the proposed method and the three baseline methods. The key characteristics of each method are also summarized on the right. * Real-world data are used; however, the training images undergo tracker artifact removal.

	Box Rotation			Drawer Opening			Cylinder Rolling
Method	SR (%) $\uparrow$	ATR (s) $\downarrow$	TPR $\uparrow$	SR (%) $\uparrow$	ATR (s) $\downarrow$	TPR $\uparrow$	SR (%) $\uparrow$	ATR (s) $\downarrow$	TPR $\uparrow$
Distill. in Sim	14.0	7.56	-0.010	-	-	-	-	-	-
IL with Human Demo	76.0	20.26	0.037	78.0	5.49	0.119	72.0	9.84	0.056
ReDOA (Ours)	70.0	8.81	0.075	64.0	2.75	0.187	68.0	8.03	0.068

TABLE I: Performance evaluation results. Results are averaged over 5 random seeds (10 trials per seed, 50 trials in total).

Image Compositing	Deploy Env	SR (%) $\uparrow$	ATR (s) $\downarrow$	TPR $\uparrow$
Proposed	Marker-Free	70.0	8.62	0.079
w/o Image Compositing	Marker-Free	53.3	8.94	0.047
w/o Image Compositing	Marker-Based	73.3	7.95	0.086

TABLE II: Ablation study evaluating the effect of marker removal. Results are averaged over 3 random seeds (10 trials per seed, 30 trials in total).

Fig. 6: Training curves of state-based SAC and image-based SAC in simulation. Curves are smoothed for visualization. State-based SAC learns substantially faster and converges to a higher return than image-based SAC.

Visuomotor Policy Learning

Visuomotor policy learning trains robots to turn camera observations, often with language and proprioception, into actions for manipulation and navigation.

See all 284 papers

VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation
May 28, 2026Victor Kowalski, Chengxi Li, Dongheui LeeCross-Modal Knowledge Distillation Robot Skill Learning
Autonomous Systems, Technische Universitaet Wien (TU Wien), Vienna, Austria · Institute of Robotics and Mechatronics (DLR), German Aerospace Center, Wessling, Germany
DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real
Sep 17, 2026Jin Wu, Lianjie Yuan, Zeyan Sun +4Sim-to-Real Transfer Synthetic Data Generation for Robotics
The University of Texas at Austin · University of Florida · BrainCo
Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience
Jun 25, 2026Raymond Yu, William Huey, Mustafa Mukadam +2RL for Robotics Robot Policy Adaptation
University of Washington · Amazon FAR

May 28, 2026J/K move · Enter open · S save

VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation

Victor Kowalski, Chengxi Li, Dongheui Lee

Autonomous Systems, Technische Universitaet Wien (TU Wien), Vienna, Austria · Institute of Robotics and Mechatronics (DLR), German Aerospace Center, Wessling, Germany

Cross-Modal Knowledge Distillation Robot Skill Learning Contact-Rich Robotic Manipulation Generalization in Robotic Manipulation Robotic RL Robot Policy Generalization+2

Open paper AskPDF arXiv

When using reinforcement learning (RL) for contact-rich robotic manipulation, vision can provide task-relevant information that complements robot proprioception. However, vision-enabled policies tend to overfit to the visual conditions seen during training, limiting their robustness and transferability. We present a human-in-the-loop RL framework that employs teacher-student distillation to achieve robust performance across multiple task variants, trained entirely in the real world without requiring domain randomization or data augmentation. A vision-enabled teacher distills its knowledge into a vision-free student that relies solely on pose, twist, and wrench sensing, combining fast training with strong task generalization. On the real-world NIST assembly benchmark board, our approach achieves 95% overall success after approximately 50 minutes of training on 3 representative tasks, including robust generalization to 8 unseen task variants. Fine-tuning with distillation achieves full success on the most challenging task. We demonstrate that the resulting policies outperform baselines in both robustness and adaptability. Page: https://tuwien-asl.github.io/VE2VF/.

Abstract

Visuomotor Policy Learning

Explore similar work

VE2VF: Vision-Enabled to Vision-Free Distillation via Real-world Reinforcement Learning for Robust Contact-Rich Manipulation

DEXTERA: From a Single Image to Deployable Dexterous Manipulation via Real-to-Sim-to-Real

Support-Constrained RL Enables Real-World Policy Improvement without Real-World Experience