Robots That Learn From Old Data: Review Maps Offline Reinforcement Learning Under Data Scarcity
Robots That Learn From Old Data: Review Maps Offline Reinforcement Learning Under Data Scarcity
A new open-access review published in Discover Data examines how offline reinforcement learning performs in data-scarce robotic manipulation settings. The authors define data-limited scenarios based on five criteria, such as limited transition tuples or reward sparsity, and analyze six algorithmic families, including conservative value-based methods, behavior-constrained approaches, and model-based techniques. The study evaluates their effectiveness on benchmark datasets like D4RL and NeoRL, noting performance variations under different data conditions.
This review provides a structured analysis of offline reinforcement learning in data-scarce robotic manipulation scenarios, highlighting key challenges like extrapolation and bootstrapping errors. It categorizes methods into algorithmic families and evaluates their performance on benchmark datasets, offering insights into their strengths and limitations under constrained conditions.
Source: Robotics — Robot learning news discovery · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
Reported success rate
<40%
View original evidence
tic constraints such as limited operator skill, scripted motion or safety monitors that cut episodes short. As reference points, the authors cite the D4RL pen-cloned variant, with roughly 5,000 transitions and a success rate below 40 percent, as a borderline case, and the relocate-cloned variant, with a success rate below 10 percent, as severely data-limited. Why does scarcity matter so much? Offline reinforcement le
Open source S5Reported success rate
<10%
View original evidence
tic constraints such as limited operator skill, scripted motion or safety monitors that cut episodes short. As reference points, the authors cite the D4RL pen-cloned variant, with roughly 5,000 transitions and a success rate below 40 percent, as a borderline case, and the relocate-cloned variant, with a success rate below 10 percent, as severely data-limited. Why does scarcity matter so much? Offline reinforcement le
Open source S5normalized returns
86–95
View original evidence
mixed-quality data, CQL achieves normalized returns of approximately 86 to 95, whereas BCQ achieves 55 to 75 under comparable settings. IQL exceeds 85 on antmaze-medium-play, outperforming TD3+BC at roughly 48. On NeoRL’s low-data split, CQL degrades fewer than 10 normalized-return points from its full-data baseline while AWAC degrades more than 20 points. MOPO and MOReL surpass behavior-constrained baselines by 5 to
Open source S19normalized returns
55–75
View original evidence
mixed-quality data, CQL achieves normalized returns of approximately 86 to 95, whereas BCQ achieves 55 to 75 under comparable settings. IQL exceeds 85 on antmaze-medium-play, outperforming TD3+BC at roughly 48. On NeoRL’s low-data split, CQL degrades fewer than 10 normalized-return points from its full-data baseline while AWAC degrades more than 20 points. MOPO and MOReL surpass behavior-constrained baselines by 5 to
Open source S19normalized returns
85
View original evidence
mixed-quality data, CQL achieves normalized returns of approximately 86 to 95, whereas BCQ achieves 55 to 75 under comparable settings. IQL exceeds 85 on antmaze-medium-play, outperforming TD3+BC at roughly 48. On NeoRL’s low-data split, CQL degrades fewer than 10 normalized-return points from its full-data baseline while AWAC degrades more than 20 points. MOPO and MOReL surpass behavior-constrained baselines by 5 to
Open source S19normalized returns
48
View original evidence
mixed-quality data, CQL achieves normalized returns of approximately 86 to 95, whereas BCQ achieves 55 to 75 under comparable settings. IQL exceeds 85 on antmaze-medium-play, outperforming TD3+BC at roughly 48. On NeoRL’s low-data split, CQL degrades fewer than 10 normalized-return points from its full-data baseline while AWAC degrades more than 20 points. MOPO and MOReL surpass behavior-constrained baselines by 5 to
Open source S19
- paper: not_reported
What remains unknown
Not established in the collected evidence: Environment, Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
tic constraints such as limited operator skill, scripted motion or safety monitors that cut episodes short. As reference points, the authors cite the D4RL pen-cloned variant, with roughly 5,000 transitions and a success rate below 40 percent, as a borderline case, and the relocate-cloned variant, with a success rate below 10 percent, as severely data-limited. Why does scarcity matter so much? Offline reinforcement le
Open source S5
mixed-quality data, CQL achieves normalized returns of approximately 86 to 95, whereas BCQ achieves 55 to 75 under comparable settings. IQL exceeds 85 on antmaze-medium-play, outperforming TD3+BC at roughly 48. On NeoRL’s low-data split, CQL degrades fewer than 10 normalized-return points from its full-data baseline while AWAC degrades more than 20 points. MOPO and MOReL surpass behavior-constrained baselines by 5 to
Open source S19
Source：Robotics — Robot learning news discovery · bioengineer.org