SPARK: Simple Post-training for Adapting pRetrained Knowledge to Robot Control
SPARK: Simple Post-training for Adapting pRetrained Knowledge to Robot Control
This research introduces SPARK, a method designed to adapt large-scale foundation models for robot control. The authors address the limitation that standard pretraining objectives emphasize global semantics over temporally sensitive representations needed for control. By incorporating temporal abstraction and knowledge preservation principles, SPARK derives compact visual state representations that retain useful prior knowledge. Experiments on various robotics benchmarks demonstrate consistent improvements in success rates and generalization compared to vanilla foundation models, with results transferring to real-world robot manipulation.
The paper proposes SPARK, a post-training method that adapts foundation models to robot control by enforcing temporal abstraction and knowledge preservation, with experiments reported across multiple benchmarks and real-world manipulation tasks.
Source: arXiv Robotics — research abstracts · Read original article ↗
Loading article text…
What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- paper: not_reported
- code: not_reported
- weights: not_reported
- dataset: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
ledge. Experiments across multiple robotics benchmarks show that SPARK consistently improves success rates and generalization over vanilla foundation models, and further demonstrate that these gains transfer to real-world robot manipulation.
Open source S5
bot models from scratch is also undesirable due to the limited scale and high collection cost of robotics data, as well as the substantial computational burden of pre-training. In this paper, we propose SPARK (Simple Post-training for Adapting pRetrained Knowledge), a post-training method that adapts foundation models to robot control. SPARK is built on two principles: temporal abstraction, which encourages the encod
Open source S3
Source：arXiv Robotics — research abstracts · arxiv.org