The authors used a vision-language model to predict physical events and their timing, or to estimate object displacement directly.
Predicting future object motion helps robots avoid emerging hazards
The proposed safety filter combines vision-language predictions, calibrated uncertainty bounds and robot dynamics to act before hazards enter the robot’s path.
Top University
Taekyung Kim · Salem Fradi · Yanning Dai · Mateusz Ostaszewski · Jürgen Schmidhuber
University of Michigan · King Abdullah University of Science and Technology (KAUST) · Dalle Molle Institute for Artificial Intelligence Research (IDSIA) · Università della Svizzera italiana (USI) · Scuola universitaria professionale della Svizzera italiana (SUPSI)
Research Digest··2 min read
Kim et al.
Why this paper
From University of Michigan and 4 others
In one line
Predictive Semantic Safety uses vision-language models and conformal prediction to filter robot commands against predicted future hazards, achieving 99.3% safe episodes.
What we could check
- ·No code link found
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- ✓Limitations stated by the authors
- ✓Reports numbers on named benchmarks
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