The authors address the challenge of long-horizon robotic manipulation requiring memory of past states and actions.
Saliency-driven workspace tokens give robots lightweight task memory
Authors show that distilling vision-language model queries into a compact latent token during training enables memory-intensive manipulation without in-the-loop VLM reasoning.
Top University
Nitish Dashora · Douglas Chen · Idan Shenfeld · John Marangola · Pulkit Agrawal · Max Simchowitz
Massachusetts Institute of Technology · Carnegie Mellon University
Research Digest··1 min read
Thread:Memory Management for Agents
Dashora et al.
Why this paper
From Carnegie Mellon University and Massachusetts Institute of Technology · Part of Memory Management for Agents, now 37 papers
In one line
A lightweight latent memory, the workspace token, can replace VLM-in-the-loop history reasoning in robot policies, improving speed and task performance.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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