Kevin Tracy presenting our entry guidance work AIAA #AIAASciTech

📢Excited to share our #ICRA2023 work on robotic table wiping via RL + optimal control! 📖 arxiv.org/abs/2210.10865 🎥 youtu.be/inORKP4F3EI 💡RL (for high-level planning) + trajectory optimization (for precise control) can solve complex tasks without on-robot data collection ⬇️

What happens when we train the largest vision-language model and add in robot experiences? The result is PaLM-E 🌴🤖, a 562-billion parameter, general-purpose, embodied visual-language generalist - across robotics, vision, and language. Website: palm-e.github.io

Introducing 𝗥𝗼𝗯𝗼𝗣𝗶𝗮𝗻𝗶𝘀𝘁 🎹🤖, a new benchmark for high-dimensional robot control! Solving it requires mastering the piano with two anthropomorphic hands. This has been one year in the making, and I couldn’t be happier to release it today! Some highlights below:

Some screen-captures of MuJoCo MPC's Quadruped task. 100% open-source, go play! youtu.be/esLuwaWz4oE

want your next robot 🤖 to move like your favorite pet 🐶? youtube videos might be all you need. checkout our recent work -- SLoMo paper: rexlab.ri.cmu.edu/papers/slomo.p… website: slomo-www.github.io/website/ video: youtu.be/bvoM-nBd7lM?si…

Excited to share our latest work, Vision-Language Frontier Maps – a SOTA approach for semantic navigation in robotics. VLFM enables robots to navigate and find objects in novel environments using vision-language foundation models, zero-shot! Accepted to #ICRA2024! 🧵

Really cool work on bimanual dexterity!!

Excited to share our new📰, DROP: Dexterous Reorientation via Online Planning! Overview: 🔹We tackle cube rotation🧊♻️on hardware 🔹DROP is the first 🧊♻️sampling-based MPC demo. No reinforcement learning! 🔹Median 30.5 rotations w/o dropping, max of 81👑🦾 See 🧵below👇

We're presenting Jacta: a versatile planner for learning dexterous and whole-body manipulation this week at CoRL! website jacta-manipulation.github.io paper arxiv.org/abs/2408.01258

ICYMI: For #CoRL2024 we released a dataset of 3.5M (!) dexterous grasps, with multi-trial labels and perceptual data for 4.3k objects. Our takeaways: scale matters, and refining grasps > better sampling. Hoping our data can enable more vision-based grasps in hardware!

Learning bimanual, contact-rich robot manipulation policies that generalize over diverse objects has long been a challenge. Excited to share our work: Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation! glide-manip.github.io 🧵1/n

Our new paper shows how task representations learned via temporal alignment enable compositional generalization for conditional policies. This allows robots to solve compound tasks by implicitly decomposing them into subtasks.