REVIEW 6 cited by
AutoEval: Autonomous Evaluation of Generalist Robot Manipulation Policies in the Real World
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Scalable and reproducible policy evaluation has been a long-standing challenge in robot learning. Evaluations are critical to assess progress and build better policies, but evaluation in the real world, especially at a scale that would provide statistically reliable results, is costly in terms of human time and hard to obtain. Evaluation of increasingly generalist robot policies requires an increasingly diverse repertoire of evaluation environments, making the evaluation bottleneck even more pronounced. To make real-world evaluation of robotic policies more practical, we propose AutoEval, a system to autonomously evaluate generalist robot policies around the clock with minimal human intervention. Users interact with AutoEval by submitting evaluation jobs to the AutoEval queue, much like how software jobs are submitted with a cluster scheduling system, and AutoEval will schedule the policies for evaluation within a framework supplying automatic success detection and automatic scene resets. We show that AutoEval can nearly fully eliminate human involvement in the evaluation process, permitting around the clock evaluations, and the evaluation results correspond closely to ground truth evaluations conducted by hand. To facilitate the evaluation of generalist policies in the robotics community, we provide public access to multiple AutoEval scenes in the popular BridgeData robot setup with WidowX robot arms. In the future, we hope that AutoEval scenes can be set up across institutions to form a diverse and distributed evaluation network.
Forward citations
Cited by 6 Pith papers
-
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Step Forcing trains a few-step autoregressive video world model so RoboWorld closed-loop rollouts plus a task-progress VLM judge recover real-world policy rankings at r=0.989 and ρ=0.970.
-
Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
A 10,300-demonstration, 260-task multimodal humanoid manipulation dataset with baseline policy evaluations and a cloud evaluation platform.
-
CRAFT: Coaching Reinforcement Learning Autonomously using Foundation Models for Multi-Robot Coordination Tasks
An LLM/VLM coaching loop that generates curricula and reward functions enabled MARL agents to learn coordinated gate passing, seesaw balancing, and bimanual pot lifting, with one policy transferred to real quadrupeds.
-
CogVLA: Cognition-Aligned Vision-Language-Action Model via Instruction-Driven Routing & Sparsification
CogVLA pairs instruction-conditioned visual-token aggregation (EFA-Routing) with transformer-layer pruning (LFP-Routing) and bidirectional action decoding (CAtten), reporting LIBERO 97.4%, real-world 70.0%, 2.5x less ...
-
WorldEval: World Model as Real-World Robot Policies Evaluator
WorldEval conditions a video generation model on a policy's internal action embeddings (Policy2Vec) and shows generated-video success rates correlate with real-world robot success rates.
-
FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing
FaceAnonyMixer claims a cancelable face generation method that irreversibly mixes real latent codes with key-derived synthetic codes for privacy-preserving face recognition.
Discussion (0). Continue with ORCID to comment.