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Daniel Brown

@daniel-brown.bsky.social
587 followers 13 following 51 posts

CS assistant prof @Utah. Researches human-robot interaction, human-in-the-loop ML, AI safety and alignment. users.cs.utah.edu/~dsbrown

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Daniel Brown @daniel-brown.bsky.social · 03/06/2026
Idea: compose safe skills instead of combining safety rewards. First learn preference-aligned skills that capture different user preferences from crowd data. Then compose these skills to solve new downstream tasks. The high-level policy solves the task. The low-level skills provide safety. 🧵6/7
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Daniel Brown @daniel-brown.bsky.social · 03/06/2026
Why? The reward learned from crowd preferences inevitably entangles shared safety criteria with user-specific preferences. When preference data are imbalanced, it can overfit to the majority user’s objective, hurting downstream task performance and making the reward trade-off hard to tune. 🧵5/7
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Daniel Brown @daniel-brown.bsky.social · 03/06/2026
Our key intuition: crowd preferences may contain diverse user goals, but shared safety criteria. Different users may prefer different trajectories for their own purposes, yet consistently avoid unsafe regions or actions. 🧵3/7
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Daniel Brown @daniel-brown.bsky.social · 03/06/2026
Our paper "A Multi-Layer Sim-to-Real Framework for Gaze-Driven Assistive Neck Exoskeletons" is being presented this week at ICRA 2026! arxiv.org/abs/2603.06779 🧵1/8
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Daniel Brown @daniel-brown.bsky.social · 23/03/2026
- On physical robots, R2BC outperforms centralized BC by 3.25x and 5.9x on two cooperative tasks. - The variability introduced by autonomous teammates during training acts as a natural form of data augmentation, making R2BC policies more robust. 8/N
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Daniel Brown @daniel-brown.bsky.social · 23/03/2026
Not only does this work, our method is simpler and better than giving an "oracle" demonstrator control of the whole team simultaneously in an offline setting. 6/N
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Daniel Brown @daniel-brown.bsky.social · 23/03/2026
How can you do imitation learning for multi-agent systems if the demonstrator is just a single human? Unless you're a doctor octopus genius, this is really hard! We study this problem in a new paper that will be at ICRA'26! 1/N
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Daniel Brown @daniel-brown.bsky.social · 10/10/2025
The main idea was that for linear rewards, we can determine, via an intersection of half-spaces, the set of reward functions that make a policy optimal and that this set of rewards is defined by a small number of "non-redundant" demonstrations or comparisons. 4/8
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Daniel Brown @daniel-brown.bsky.social · 10/10/2025
It was a fun paper and has some interesting nuggets, like the fact that there exist sufficient conditions under which we can verify exact and approximate AI alignment across an infinite set of deployment environments via a constant-query-complexity test. 3/8
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Daniel Brown @daniel-brown.bsky.social · 10/10/2025
Can you trust your reward model alignment scores? New work presented today at the COLM Workshop on Socially Responsible Language Modelling Research led by Purbid Bambroo and in collaboration with @anamarasovic.bsky.social that probes LLM preference test sets for redundancy and inflated scores. 1/8
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Daniel Brown @daniel-brown.bsky.social · 29/09/2025
We apply our approach to surgically-inspired deformable tissue manipulation and find it achieves a 10% lower reliance on human interventions compared to prior work that leverages variance-based uncertainty estimates. 3/5
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Daniel Brown @daniel-brown.bsky.social · 29/09/2025
Check out our new paper being presented today at #CoRL2025 on uncertainty quantification: openreview.net/forum?id=K7K.... We propose a new second-order metric for uncertainty quantification in robot learning that we call "Agreement Volatility." 1/5
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Daniel Brown @daniel-brown.bsky.social · 03/03/2025
If you're in Melbourne, come check out Connor's talk in the Teleoperation and Shared Control session today! Paper: arxiv.org/abs/2501.08389 Website: sites.google.com/view/zerosho... This is joint work with two of my other amazing PhD students Zohre Karimi and Atharv Belsare! 3/3
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Daniel Brown @daniel-brown.bsky.social · 03/03/2025
We study how to enable robots to use end-effector vision to estimate zero-shot human intents in conjunction with blended control to help humans accomplish manipulation tasks like grocery shelving with unknown and dynamically changing object locations. 2/3
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Daniel Brown @daniel-brown.bsky.social · 03/03/2025
Shared autonomy systems have been around for a long time but most approaches require a learned or specified set of possible human goals or intents. I'm excited for my student Connor Mattson to present our work at #HRI2025 on a zero-shot, vision-only shared autonomy (VOSA) framework. 1/3
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