Tom Silver
@tomssilver.bsky.social
Assistant Professor @Princeton. Developing robots that plan and learn to help people. Prev: @Cornell, @MIT, @Harvard.
https://tomsilver.github.io/
https://tomsilver.github.io/
This project was led by a truly exceptional Princeton undergrad @yijieisabelliu.bsky.social, who is looking for PhD opportunities this year! Her website: isabelliu0.github.io
Y. Isabel Liu 刘亦颉
Y. Isabel Liu - PhD Applicant in Robotics. Princeton University Computer Science undergraduate researching task and motion planning, dexterous manipulation, and learning for robotics.
isabelliu0.github.io
November 5, 2025 at 3:22 PM
This project was led by a truly exceptional Princeton undergrad @yijieisabelliu.bsky.social, who is looking for PhD opportunities this year! Her website: isabelliu0.github.io
Good question (and sorry I missed your reply!). Random ideas:
1. Revisit PSRs in the context of (neural) representation learning for RL, e.g., arxiv.org/abs/2508.13113
2. PSRs for learning abstractions for planning under partial observability, e.g., your work with Yixuan (arxiv.org/abs/2408.14769)
1. Revisit PSRs in the context of (neural) representation learning for RL, e.g., arxiv.org/abs/2508.13113
2. PSRs for learning abstractions for planning under partial observability, e.g., your work with Yixuan (arxiv.org/abs/2408.14769)
Points2Plans: From Point Clouds to Long-Horizon Plans with Composable Relational Dynamics
We present Points2Plans, a framework for composable planning with a relational dynamics model that enables robots to solve long-horizon manipulation tasks from partial-view point clouds. Given a langu...
arxiv.org
October 12, 2025 at 12:47 PM
Good question (and sorry I missed your reply!). Random ideas:
1. Revisit PSRs in the context of (neural) representation learning for RL, e.g., arxiv.org/abs/2508.13113
2. PSRs for learning abstractions for planning under partial observability, e.g., your work with Yixuan (arxiv.org/abs/2408.14769)
1. Revisit PSRs in the context of (neural) representation learning for RL, e.g., arxiv.org/abs/2508.13113
2. PSRs for learning abstractions for planning under partial observability, e.g., your work with Yixuan (arxiv.org/abs/2408.14769)
I’m doing this in my course right now. So far so good! One finding: if students try to install and uv while already being in a conda env, bad things happen. Make sure to deactivate conda first.
September 21, 2025 at 10:35 PM
I’m doing this in my course right now. So far so good! One finding: if students try to install and uv while already being in a conda env, bad things happen. Make sure to deactivate conda first.