Beyond Information Sufficiency: Observation-Action Space Alignment in Robotic Reinforcement Learning

Vishal Bhat, Zahra Suleymanova, Colin Bellinger
Proceedings of the The 39th Canadian Conference on Artificial Intelligence, PMLR 318:1052-1059, 2026.

Abstract

Observation design is a fundamental yet under-specified component of robotic reinforcement learning (RL). While classical theory emphasizes that observations should be informationally sufficient, we show—through a focused reaching case study—that sufficiency alone does not guarantee learnability or sim-to-real transfer. Using PPO on a 6-DOF Kinova Gen3 Lite arm, we demonstrate that two observation spaces with equal dimension-ality and theoretically equivalent information content (9D joint-based vs. 9D Cartesian- based) differ by over 60 percentage points in success when paired with Cartesian velocity control. Aligned Cartesian observations consistently learn faster, achieve higher success, and transfer zero-shot to the physical robot, whereas misaligned joint observations fail despite being sufficient in principle. Our findings highlight representational alignment between observations, actions, and rewards as a first-order design constraint in robotic RL, demonstrated through controlled simulation and zero-shot real-world deployment.

Cite this Paper

BibTeX
@InProceedings{pmlr-v318-bhat26a, title = {Beyond Information Sufficiency: Observation-Action Space Alignment in Robotic Reinforcement Learning}, author = {Bhat, Vishal and Suleymanova, Zahra and Bellinger, Colin}, booktitle = {Proceedings of the The 39th Canadian Conference on Artificial Intelligence}, pages = {1052--1059}, year = {2026}, editor = {Bouzar-Benlabiod, Lydia and Leung, Carson}, volume = {318}, series = {Proceedings of Machine Learning Research}, month = {25--29 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v318/main/assets/bhat26a/bhat26a.pdf}, url = {https://proceedings.mlr.press/v318/bhat26a.html}, abstract = {Observation design is a fundamental yet under-specified component of robotic reinforcement learning (RL). While classical theory emphasizes that observations should be informationally sufficient, we show—through a focused reaching case study—that sufficiency alone does not guarantee learnability or sim-to-real transfer. Using PPO on a 6-DOF Kinova Gen3 Lite arm, we demonstrate that two observation spaces with equal dimension-ality and theoretically equivalent information content (9D joint-based vs. 9D Cartesian- based) differ by over 60 percentage points in success when paired with Cartesian velocity control. Aligned Cartesian observations consistently learn faster, achieve higher success, and transfer zero-shot to the physical robot, whereas misaligned joint observations fail despite being sufficient in principle. Our findings highlight representational alignment between observations, actions, and rewards as a first-order design constraint in robotic RL, demonstrated through controlled simulation and zero-shot real-world deployment.} }
Endnote
%0 Conference Paper %T Beyond Information Sufficiency: Observation-Action Space Alignment in Robotic Reinforcement Learning %A Vishal Bhat %A Zahra Suleymanova %A Colin Bellinger %B Proceedings of the The 39th Canadian Conference on Artificial Intelligence %C Proceedings of Machine Learning Research %D 2026 %E Lydia Bouzar-Benlabiod %E Carson Leung %F pmlr-v318-bhat26a %I PMLR %P 1052--1059 %U https://proceedings.mlr.press/v318/bhat26a.html %V 318 %X Observation design is a fundamental yet under-specified component of robotic reinforcement learning (RL). While classical theory emphasizes that observations should be informationally sufficient, we show—through a focused reaching case study—that sufficiency alone does not guarantee learnability or sim-to-real transfer. Using PPO on a 6-DOF Kinova Gen3 Lite arm, we demonstrate that two observation spaces with equal dimension-ality and theoretically equivalent information content (9D joint-based vs. 9D Cartesian- based) differ by over 60 percentage points in success when paired with Cartesian velocity control. Aligned Cartesian observations consistently learn faster, achieve higher success, and transfer zero-shot to the physical robot, whereas misaligned joint observations fail despite being sufficient in principle. Our findings highlight representational alignment between observations, actions, and rewards as a first-order design constraint in robotic RL, demonstrated through controlled simulation and zero-shot real-world deployment.
APA
Bhat, V., Suleymanova, Z. & Bellinger, C.. (2026). Beyond Information Sufficiency: Observation-Action Space Alignment in Robotic Reinforcement Learning. Proceedings of the The 39th Canadian Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 318:1052-1059 Available from https://proceedings.mlr.press/v318/bhat26a.html.

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