Benchmarking and Enhancing Skill-Level Memory for Partially Observable Robotic Manipulation

Hugging Face Daily Papers Papers
robotics manipulation benchmark memory partial-observability reinforcement-learning

Summary

The paper introduces HIDE, a benchmark for evaluating manipulation memory under partial observability, and SEEK, a framework combining three memory mechanisms that improves task success in simulation and real-world robotic manipulation experiments.

Recent advances in robot learning have enabled manipulation policies to perform increasingly diverse tasks and generalize across environments. However, reliable execution often depends on hidden task states that cannot be determined from current observations alone, making interaction history essential. We introduce HIDE, a benchmark for evaluating manipulation memory under partial observability. HIDE comprises 15 tasks covering repetition counting, historical-state recall, and execution-progress tracking, with randomized initial configurations and decision points where similar observations require different actions depending on prior events. We further propose SEEK, a framework combining three complementary memory mechanisms to retain historical evidence and track execution state. Evaluations reveal substantial limitations in existing policies on HIDE, while memory augmentation improves task success in both simulation and real-world experiments. Individual mechanisms benefit some tasks but can degrade others; their combination achieves the highest average success rate on HIDE among the evaluated configurations. These findings highlight the importance of maintaining internal representations of hidden task states and matching memory design to task-specific information requirements.
Original Article
View Cached Full Text

Cached at: 10/02/26, 08:27 AM

Paper page - Benchmarking and Enhancing Skill-Level Memory for Partially Observable Robotic Manipulation

Source: https://huggingface.co/papers/2609.38886

Abstract

Recentadvancesinrobotlearninghaveenabledmanipulationpoliciestoperformincreasinglydiversetasksandgeneralizeacrossenvironments.However,reliableexecutionoftendependsonhiddentaskstatesthatcannotbedeterminedfromcurrentobservationsalone,makinginteractionhistoryessential.WeintroduceHIDE,abenchmarkforevaluatingmanipulationmemoryunderpartialobservability.HIDEcomprises15taskscoveringrepetitioncounting,historical-staterecall,andexecution-progresstracking,withrandomizedinitialconfigurationsanddecisionpointswheresimilarobservationsrequiredifferentactionsdependingonpriorevents.WefurtherproposeSEEK,aframeworkcombiningthreecomplementarymemorymechanismstoretainhistoricalevidenceandtrackexecutionstate.EvaluationsrevealsubstantiallimitationsinexistingpoliciesonHIDE,whilememoryaugmentationimprovestasksuccessinbothsimulationandreal-worldexperiments.Individualmechanismsbenefitsometasksbutcandegradeothers;theircombinationachievesthehighestaveragesuccessrateonHIDEamongtheevaluatedconfigurations.Thesefindingshighlighttheimportanceofmaintaininginternalrepresentationsofhiddentaskstatesandmatchingmemorydesigntotask-specificinformationrequirements.

View arXiv pageView PDFProject pageGitHub1Add to collection

Get this paper in your agent:

hf papers read 2609\.38886

Don’t have the latest CLI?curl \-LsSf https://hf\.co/cli/install\.sh \| bash

Models citing this paper0

No model linking this paper

Cite arxiv.org/abs/2609.38886 in a model README.md to link it from this page.

Datasets citing this paper0

No dataset linking this paper

Cite arxiv.org/abs/2609.38886 in a dataset README.md to link it from this page.

Spaces citing this paper0

No Space linking this paper

Cite arxiv.org/abs/2609.38886 in a Space README.md to link it from this page.

Collections including this paper0

No Collection including this paper

Add this paper to acollectionto link it from this page.

Similar Articles

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark

Hugging Face Daily Papers

RoboMemArena introduces a large-scale benchmark for evaluating robotic memory across 26 complex tasks with real-world validation, alongside PrediMem, a dual-system vision-language-action model that improves memory management through predictive coding.