6 accepted papers
Generative models have emerged as a powerful paradigm for AI planning, yet their performance remains constrained by training data distribution. One approach is to improve generated solutions during inference by scaling test-time compute. A more efficient alternative is to optimize the inferential pr…
Sampling-based motion planning algorithms excel at searching global solution paths in geometrically complex settings. However, classical approaches, such as RRT, are difficult to scale beyond low-dimensional search spaces and rely on privileged knowledge e.g. about collision detection and underlying…
Planning enables autonomous agents to solve complex decision-making problems by evaluating predictions of the future. However, classical planning algorithms often become infeasible in real-world settings where state spaces are high-dimensional and transition dynamics unknown. The idea behind latent…
Planning algorithms are powerful at solving long-horizon decision-making problems but require that environment dynamics are known. Model-free reinforcement learning has recently been merged with graph-based planning to increase the robustness of trained policies in state-space navigation problems. R
Recent advances in machine learning have triggered an enormous interest in using learning-based approaches for robot control and object manipulation. While the majority of existing algorithms are evaluated under the assumption that the involved bodies are rigid, a large number of practical applicati…
Deep Generative Models such as Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) have found multiple applications in Robotics, with recent works suggesting the potential use of these methods as a generic solution for the estimation of sampling distributions for motion planning…