Can robotics overcome its data scarcity problem? скачать в хорошем качестве

Can robotics overcome its data scarcity problem?

Robotics has a data problem. How are researchers planning to overcome it? David Watkins - Robotics Ph.D. Candidate https://davidjosephwatkins.com/ The Man Who Made Robots Dance Now Wants Them to Think for Themselves https://www.wired.com/story/boston-dy... Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware https://tonyzhaozh.github.io/aloha/ Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware [paper] https://arxiv.org/abs/2304.13705 Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots https://umi-gripper.github.io/ Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots [paper] https://umi-gripper.github.io/umi.pdf RT-1: Robotics Transformer for Real-World Control at Scale https://robotics-transformer1.github.io/ RT-1: Robotics Transformer for Real-World Control at Scale [paper] https://arxiv.org/pdf/2212.06817.pdf RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control [paper] https://arxiv.org/abs/2307.15818 AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents https://arxiv.org/abs/2401.12963 How the brain works: the brain is mostly for movement http://www.educationalneuroscience.or... #ai #robotics #robotarms 0:00 Intro 0:30 Contents 0:37 Part 1: The data dilemma 0:51 The AI Institute 1:43 Dexterity is the new challenge 2:05 Cerebellum responsible for movement 2:35 Scaling balance models to dexterity 2:54 Video generation models as a data source 3:20 Robots rely on vision rather than tactile feedback 3:47 Run simulations to gather data 4:16 Supervised and unsupervised data collection 5:01 Human-directed training through teleoperation 5:33 Part 2: Real world data collection 5:51 Paper: Aloha: use multiple robot arms 6:58 Analysis of Aloha 7:29 Paper: UMI gripper: copy human movements 8:34 Paper: RT-1: data collection at scale from Google 8:59 Robot arms in robot classrooms (unsupervised) 10:09 Part 3: Lessons from LLMs 10:20 Paper: RT-2: leverage GPT innovations 11:23 Paper: AutoRT: leverage foundation LLM 12:09 Low success rate at tasks 13:00 Robot constitution 13:37 Can large language models drive robots? 14:34 Robot foundation models are far behind 15:25 Smarter representations to handle scarce data 16:04 Data/compute trade-off 17:15 Many correlations in physical space 17:40 Models are not good at few-shot learning 18:13 Stretch data without needing parameters 18:29 Conclusion 19:06 Real-world data collection 19:44 Google experiments 20:41 Outro