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open teach a versatile teleoperation system for robotic manipulation open teach a versatile teleoperation system for robotic manipulation aadhithya iyer 1 zhuoran peng 1 yinlong dai 1 irmak guzey 1 siddhant haldar 1 soumith chintala 2 lerrel pinto 1 1 new york university 2 meta description arxiv code code abstract open sourced user friendly tools form the bedrock of scientific advancement across disciplines the widespread adoption of data driven learning has led to remarkable progress in multi fingered dexterity bimanual manipulation and applica tions ranging from logistics to home robotics however existing data collection platforms are often proprietary costly or tailored to specific robotic morphologies we present open teach a new teleoperation system leveraging vr headsets to immerse users in mixed reality for intuitive robot control built on the affordable meta quest 3 which costs 500 open teach enables real time control of various robots including multi fingered hands bimanual arms and mobile manipulators through an easy to use app using natural hand gestures and movements users can manipulate robots at up to 90hz with smooth visual feedback and interface widgets offering closeup environment views we demonstrate the versatility of open teach across 38 tasks on different robots a comprehensive user study indicates significant improvement in teleoperation capability over the anyteleop framework further experiments exhibit that the collected data is compatible with policy learning on 10 dexterous and contact rich manipulation tasks currently supporting franka xarm jaco allegro and hello stretch platforms open teach is fully open sourced to promote broader adoption hardware open teach utilizes the quest 3 vr headset for teleoperation providing low latency and high frequency visual feedback the quest 3 is chosen for its affordability and accessibility below we provide videos of the full color passthrough provided by quest 3 and the user interface of the open teach app as shown we implement a screen within the vr app that displays the feed from a camera positioned within the scene this enables the user to view a robotic scene both through their eyes via visual passthrough as well as realtime streams from the robot s camera we emphasize the exceptional clarity of the quest 3 visual passthrough enabling users to perceive tasks in a 3d environment rather than solely on a 2d screen furthermore the high frequency low latency teleoperation facilitated by open teach allows users to observe real time movements of the robot enabling immediate corrections for any errors in its pose passthrough videos make sandwich 8x pack and lift a basket 4x iron towel 4x your browser does not support the video tag tasks utilizing open teach we execute a total of 38 tasks across three real world environments and two simulated platforms each featuring distinct robot configurations this underscores open teach s role as a versatile teleoperation platform seamlessly compatible with various robot platforms and morphologies franka allegro task pack and lift a basket 8x put muffin in the oven 8x pick up tea satchet 1x open box 2x undo latch and open box 4x slot battery 2x grasp object and twist 2x grasp sponge 2x shell game 8x your browser does not support the video tag kinova allegro task iron towel 4x open laptop 2x open box and pick object 8x stack blocks 8x write alphabet 4x hit hammer 8x unfold towel 8x open ketchup bottle 8x open drawer 8x your browser does not support the video tag bimanual task make sandwich 8x insert usb 2x slice cucumber 8x toast bread 4x sweep dirt off the table 2x unfold cloth 4x handover can 4x uncap marker 2x pour sprinkles 2x your browser does not support the video tag allegro sim task flip cube grasp sponge pinch grasp your browser does not support the video tag libero sim task organize basket put pan on stove and turn it on place white mug on left plate turn on stove close top drawer of cabinet your browser does not support the video tag hello stretch task door opening drawer opening pick and drop trash your browser does not support the video tag policy learning in addition to collecting demonstrations we highlight the value of collected data in policy training across the franka allegro setup and two simulated environments libero and allegro sim using behavior cloning and inverse reinforcement learning we train policies for 10 tasks achieving an impressive average success rate of 87 this validates the efficacy of the data acquired via open teach for policy learning task pick up tea satchet 1x open box 2x grasp sponge 2x grasp object and twist 2x flip cube grasp sponge pinch grasp close top drawer of cabinet turn on stove pick and place soup into basket your browser does not support the video tag comparison with baselines we provide a comparison between existing teleoperation systems and observe that open teach is the only one that is calibration free support multiple arms hands and mobile manipulators and most importantly is fully open source user study we conducted a user study with 15 participants to assess the intuitiveness and user friendliness of our framework for inexperienced users we provide task performance videos from these users overall we found that new users take 2 25x longer and achieve a success rate of 76 compared to experts this observation highlights that while our system is intuitive for new users prolonged training can lead to further improvement in their performance tasks performed by users open box of mints 2x flip cube 1x pinch grasp 1x pour 4x pick and place 2x your browser does not support the video tag bibtex misc iyer2024open title open teach a versatile teleoperation system for robotic manipulation author aadhithya iyer and zhuoran peng and yinlong dai and irmak guzey and siddhant haldar and soumith chintala and lerrel pinto year 2024 eprint 2403 07870 archiveprefix arxiv primaryclass cs ro this webpage template was recycled from here