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evaluating real world robot manipulation policies in simulation evaluating real world robot manipulation policies in simulation xuanlin li 1 kyle hsu 2 jiayuan gu 1 karl pertsch 2 3 oier mees 3 homer rich walke 3 chuyuan fu 4 ishikaa lunawat 2 isabel sieh 2 sean kirmani 4 sergey levine 3 jiajun wu 2 chelsea finn 2 hao su 1 quan vuong 4 ted xiao 4 equal contribution core contributors equal advising 1 uc san diego 2 stanford university 3 uc berkeley 4 google deepmind paper video code colab characterizing generalist robot manipulation policies typically involves evaluating them on many tasks across many scenarios a laborious undertaking in the real world top left we propose simpler a collection of simulated environments for manipulation policy evaluation on common real robot setups that shows strong correlation with real world performance top right rt2 x place apple into top drawer octo base put eggplant in basket octo small put spoon on towel rt1 x close bottom drawer octo small stack green block on yellow block rt1 move redbull can near orange abstract the field of robotics has made significant advances towards generalist robot manipulation policies however real world evaluation of such policies is not scalable and faces reproducibility challenges which are likely to worsen as policies broaden the spectrum of tasks they can perform in this work we demonstrate that simulation based evaluation can be a scalable reproducible and reliable proxy for real world evaluation we identify control and visual disparities between real and simulated environments as key challenges for reliable simulated evaluation and propose approaches for mitigating these gaps without needing to craft full fidelity digital twins of real world environments we then employ these approaches to create simpler a collection of simulated environments for manipulation policy evaluation on common real robot setups through paired sim and real evaluations of manipulation policies we demonstrate strong correlation between policy performance in simpler environments and in the real world additionally we find that simpler evaluations accurately reflect real world policy behavior modes such as sensitivity to various distribution shifts we open source all simpler environments along with our workflow for creating new environment to facilitate research on general purpose manipulation policies and simulated evaluation frameworks video approach we introduce simpler a suite of open source simulated evaluation environments for common real robot manipulation setups all environments expose a uniform gym api additionally we open source policy inference code e g rt 1 rt 1 x octo for real to sim evaluation and we provide a detailed guide for evaluating new policies and creating new evaluation environments all environments can be imported with a single line of code and can be interacted with through a standard gym interface metrics for real to sim evaluation an effective useful simulation based evaluation should demonstrate good correlations in policy ranking performance with real evaluations to measure such correlations one can apply the traditional pearson correlation metric r but it has the following limitations 1 pearson correlation only assess the linear fit between real and sim performances while for simulated evaluation we don t necessarily need linear correlations as long as sim eval reflects real world performance improvements between different policies middle right 2 pearson correlation does not reflect the range of values it is computed over for policy sets that perform closely in real far right pearson r may change drastically based on small real world performance differences which can often be attributed to the inherent noise in real world evaluations thus we introduce the mean maximum rank violation mmrv metric lower the better to better assess the real and sim policy ranking consistency the key underlying quantity is the rank violation between two policies which weighs the significance of the simulator incorrectly ranking the policies by the corresponding margin in real world performance mmrv then aggregates the n 2 rank violations by averaging the worst case rank violation for each policy visual matching mitigates the real to sim visual gap visual discrepancies between real world and simulated environments can comprise a distribution shift that adversely affects a learned policy s behavior rendering simulated evaluation unreliable our goal is to match the simulator visuals to those of the real world environment with only a modest amount of manual effort our proposed visual matching consists of 1 green screening i e segmenting out interactive simulated assets and overlaying them onto real world backgrounds and 2 texture matching which involves projecting real object textures onto simulation assets and tuning robot arm colors using real videos system identification mitigates the real to sim control gap the goal of mitigating the control gap between simulated and real world environments is to ensure that policy actions executed in simulation yields comparable effects on the robot s end effector as those observed when executed on the real robot we perform system identification sysid for closing the control gap between real and simulated environments on a small sample of trajectories from the real world dataset real world rollout control without sysid control with sysid applications evaluating and comparing policies simpler can be used to evaluate diverse sets of rigid body tasks non articulated articulated objects tabletop non tabletop tasks shorter longer horizon tasks with many intra task variations e g different object combinations different object robot positions and orientations for each of two robot embodiments google robot and widowx simpler can also compare the performance of different policies and perform checkpoint selection policy performances evaluated in simpler have strong correlation with those in the real world illustrated by low mmrv and high pearson r in the figures below analyzing and predicting policy behaviors under distribution shifts simpler can be used to analyze the policies finegrained behaviors such as their robustness to common distribution shifts like lightings backgrounds camera poses distractor objects and table textures fig left the findings from simpler are highly correlated with those in the real world illustrated by low mmrv and high pearson r additionally simpler can predict policy behaviors under novel distribution shifts such as changes in arm textures fig right gallery paired evaluations in real and sim simpler yields a strong correlation between real world and simulated performance across 1500 evaluation episodes from each of real and sim here we illustrate a few paired evaluations in real and sim google robot control frequency 3hz widowx control frequency 5hz each video frame corresponds to a control step real world rollouts for google robot simulation rollouts for google robot real world rollouts for widowx simulation rollouts for widowx bibtex article li24simpler title evaluating real world robot manipulation policies in simulation author xuanlin li and kyle hsu and jiayuan gu and karl pertsch and oier mees and homer rich walke and chuyuan fu and ishikaa lunawat and isabel sieh and sean kirmani and sergey levine and jiajun wu and chelsea finn and hao su and quan vuong and ted xiao journal arxiv preprint arxiv 2405 05941 year 2024 website borrowed from nerfies under a creative commons attribution sharealike 4 0 international