Cave

[Lisa Lee @rl_agent]

What makes it hard for robots to generalize to new environments?

In our study, we broke down the notion of an “environment” into smaller, more manageable factors of variation, such as lighting or camera placement.

https://t.co/08SD44xL5D

1/n

[Chris Paxton @chris_j_paxton]

The future of robot butlers starts with mobile manipulation.
We’re announcing the NeurIPS 2023 Open-Vocabulary Mobile Manipulation Challenge!
- Full robot stack ✅
- Parallel sim and real evaluation ✅
- No robot required ✅👀

https://t.co/mggAbRhrLP

[Deepak Pathak @deepakpathak]

After working on curiosity-driven learning for many years, we finally scale it to real robot control.

In our #ICRA2023 paper, we present ALAN which explores w/o any rewards to collect its own real-world data & then repurposes its experience to achieve goals at deployment.

[Deepak Pathak @deepakpathak]

How can we enable robots to perform diverse tasks? Designing rewards or demos for each task is not scalable.

We propose WHIRL which learns by watching a single human video followed by autonomous exploration *directly* in the real world (no simulation)!

https://t.co/7XlOf4OyEm

[Cave @xumishan]

@ryowwang @zhoubaqi @GKA9yHujB7jdfbH @9527kw @Maggie84734462 好的，谢谢提供，非常有用。现在主要是纠结去上海，厦门或者蛇口到香港机场登机

[Cave @xumishan]

@ojyamo @9527kw 请问是直接打车到香港机场吗？

[Cave @xumishan]

@ryowwang @zhoubaqi @GKA9yHujB7jdfbH @9527kw @Maggie84734462 请问你买的是单程吗？有没有检查往返机票呢？

[Sergey Levine @svlevine]

What do Lyapunov functions, offline RL, and energy based models have in common? Together, they can be used to provide long-horizon guarantees by "stabilizing" a system in high density regions! That's the idea behind Lyapunov Density Models: https://t.co/tx1RlPAxmm

A thread:

[Roozbeh Mottaghi @RoozbehMottaghi]

Massive datasets have enabled many recent advancements in computer vision and NLP. ProcTHOR presents a platform to enable similar success stories in Robotics and Embodied AI in general.

ProcTHOR website: https://t.co/ftZoi9Cmdg
Details: https://t.co/D1OlA0JLxh

[Sergey Levine @svlevine]

A talk that I preparing on how reinforcement learning can acquire abstractions for planning. Covers some HRL, trajectory transformer, offline RL: https://t.co/LKl3OgAjst

This was made for the "Bridging the Gap Between AI Planning and Reinforcement Learning" workshop at ICAPS.

[Jeannette Bohg @leto__jean]

To do daily chores, robots need to understand articulated objects. Sometimes a single picture of an object is deceiving. We propose a novel method that leverages temporal data to estimate the object articulation mechanism.

https://t.co/BBQFaSKOhh

🧵 1/9

[Ankur Handa @ankurhandos]

Factory: Fast Contact for Robotic Assembly, our recent work, is a set of simulation methods & robot learning tools for contact-rich interactions for robotic assembly. It will be presented at RSS next month.

Paper: https://t.co/WoC8E6bZle
Website: https://t.co/BfBty65hky

[Google DeepMind @GoogleDeepMind]

Gato🐈a scalable generalist agent that uses a single transformer with exactly the same weights to play Atari, follow text instructions, caption images, chat with people, control a real robot arm, and more: https://t.co/9Q7WsRBmIC

Paper: https://t.co/ecHZqzCSAm 1/

[Cave @xumishan]

@WOCAO2021 @6bcGNl9nbJnJOwU @KooPeben 他们是从哪个城市出境的呢？谢谢

[AK @_akhaliq]

How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
abs: https://t.co/AhJoIK94gS

[Cave @xumishan]

@bingyuicejade 国内哪所学校的学生？

[Science Magazine @ScienceMagazine]

#DeepLearning and manual training taught a #robot to open a door successfully more than 96% of the time, completing 15 rounds of back-and-forth in 30 minutes.

📝: https://t.co/oGy3YleJrg @SciRobotics

[Karol Hausman @hausman_k]

Super excited to introduce SayCan (https://t.co/NWyvPubhmE): 1st publication of a large effort we've been working on for 1+ years

Robots ground large language models in reality by acting as their eyes and hands while LLMs help robots execute long, abstract language instructions

[Karol Hausman @hausman_k]

Remember the kitchen env from Relay Policy Learning? This time it's in real!

In DBAP, we create system that continuously, autonomously improves on many tasks.

It's not enough to give demos to bootstrap tasks, demos should also bootstrap practicing!
https://t.co/m1F2PL5DuA
🧵👇

[Aran Komatsuzaki @arankomatsuzaki]

Reinforcement Learning with Action-Free Pre-Training from Videos

Significantly improves both final performances and sample-efficiency of vision-based RL in a variety of manipulation and locomotion tasks.

https://t.co/u1MMeLwurG