Applied Artificial Intelligence Lab (A2I)
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The Applied AI Lab (A2I) explores core challenges in AI and Machine Learning to enable robots to robustly and effectively operate in complex, real-world environments. Our research is guided by our vision to create machines which constantly improve through use in their dedicated workspace. In doing so we explore a number of intellectual challenges at the heart of robot learning such as machine introspection in perception and decision making, data efficient learning from demonstration, task-based and transfer learning and the learning of complex tasks via a curriculum of less complex ones. All the while our intellectual curiosity remains grounded in real-world robotics domains such as autonomous driving, logistics, manipulation or space exploration.

RT @Jack_T_Collins: 🧵 Introducing RAMP, an open-source robotics benchmark inspired by real-world industrial assembly tasks. Check out the…

RT @junjungoal: Excited to present AMP-LS, our recent work on gradient-based motion planning, accepted to @ieee_ras_icra 2023. AMP-LS can…
RT @IngmarPosner: Our next offering in planning for manipulation using optimisation in structured latent spaces: fast, reactive planning be…

RT @AnsonISL: Super excited to share our new work - a personal first - Variational Causal Dynamics (VCD). We trained a latent state space m…
RT @IngmarPosner: Admittedly, I've been a doubter when it comes to all things causal. That's why we set ourselves the challenge of learning…
RT @IngmarPosner: Our latest offering in efficient and accurate #radar odometry competitive for embedded devices. Nicely done @robwrobots a…