Trossen Robotics

I've been implementing the groundbreaking research from the paper "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware." at Trossen Robotics. This paper introduces the innovative Action Chunking Transformer (ACT) combined with Temporal Ensemble, pushing the boundaries of what's possible with low-precision robots in imitation learning. In traditional imitation learning, high precision is crucial for performing high dexterity tasks. Low precision often forces robots into out-of-distribution states, leading to compounding errors and task failure. ACT tackles this challenge by predicting future K steps from a current state, significantly reducing compounding errors by K folds. While this may seem like an open-loop control solution, it shines when paired with Temporal Ensemble. By aggregating actions at each time step and giving the highest weight to the oldest actions, our robot can effectively make decisions based on states within the sample distribution, resulting in more reliable task performance. Dive into the details of Action Chunking and Temporal Ensemble by checking out the paper and explore the amazing capabilities of the Aloha Kit. Paper: https://lnkd.in/dZTcdrii Aloha Kit: https://lnkd.in/d2ZH7XHa #MachineLearning #Robotics #ImitationLearning #AI #Innovation #TrossenRobotics #InternshipExperience #ActionChunkingTransformer #TemporalEnsemble #AlohaKit

I've been implementing the groundbreaking research from the paper "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware." at Trossen Robotics. This paper introduces the innovative Action Chunking Transformer (ACT) combined with Temporal Ensemble, pushing the boundaries of what's possible with low-precision robots in imitation learning. In traditional imitation learning, high precision is crucial for performing high dexterity tasks. Low precision often forces robots into out-of-distribution states, leading to compounding errors and task failure. ACT tackles this challenge by predicting future K steps from a current state, significantly reducing compounding errors by K folds. While this may seem like an open-loop control solution, it shines when paired with Temporal Ensemble. By aggregating actions at each time step and giving the highest weight to the oldest actions, our robot can effectively make decisions based on states within the sample distribution, resulting in more reliable task performance. Dive into the details of Action Chunking and Temporal Ensemble by checking out the paper and explore the amazing capabilities of the Aloha Kit. Paper: https://lnkd.in/dZTcdrii Aloha Kit: https://lnkd.in/d2ZH7XHa #MachineLearning #Robotics #ImitationLearning #AI #Innovation #TrossenRobotics #InternshipExperience #ActionChunkingTransformer #TemporalEnsemble #AlohaKit

We are currently interviewing for Mechanical Design Engineering Interns for the Fall. If you are a bright student who wants to build robot arms and robot rovers and are good at CAD/CAM we want to hear from you. We also just bought a big CNC Mill and need smart people who know how to run it! Come join the team and make cool robots with us. https://lnkd.in/g5Hf6frP

Meet the robot that can sauté shrimp 🍤 Stanford engineers created a low-cost, mobile robot that has quickly learned to do complex household tasks including cooking, putting away dishes, and cleaning up spills. Other chores aren’t far behind … “Helping robots is a very promising future of the field where we – as AI researchers and roboticists – can make a positive impact in society,” says Zipeng Fu, Stanford University School of Engineering computer science graduate student. 🎥: Kurt Hickman

🤗 Let's play Lego now! https://lnkd.in/euepcQfb (video speedup by 2x)