Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning

• Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. This leads to blazing fast training times for complex robotics tasks on a single GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a CPU based simulator and GPU for neural networks. We host the results and videos at this https URL and isaac gym can be downloaded at this https URL.

SBIRs

• 1:00 meeting with Rukan
• Write some on the paper
• Do slides for demos
  • Add ‘assist Steve’ story
• Update repo and switch to dev. Verify that everything still works – it does! And receives messages as well. Oddly it seems to b e splitting the messages between the Python and TypeScript listeners:

GPT Agents

• Make some spreadsheets that compare the stars/sentiment properties of the relative models. Done. The models are remarkably stable, even down to 3k. They make more mistakes with the specific meta training but that seems to be about it?
• Trying to generate reviews from the untrained gpt2 models. The 117M model was (probably?) too small, so I’m trying the 774M model without finetuning. It requires two passes – the first creates the review (using a bigger prompt), and then I use the result and tack on “{}. I give it a star rating of“. Then I need to parse the ratings, which can be numbers or strings. I’ve kind of run out of energy so I’ll finish later.
• Start trying to figure out a posterior test?