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Found 6 papers, 2 papers with code
Embedding Synthetic Off-Policy Experience for Autonomous Driving via Zero-Shot Curricula
However, this approach produces agents that do not perform robustly in safety-critical settings, an issue that cannot be addressed by simply adding more data to the training set - we show that an agent trained using only a 10% subset of the data performs just as well as an agent trained on the entire dataset.
Hierarchical Model-Based Imitation Learning for Planning in Autonomous Driving
We demonstrate the first large-scale application of model-based generative adversarial imitation learning (MGAIL) to the task of dense urban self-driving.
STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead
The proposed architecture divides human motion prediction into two parts: 1) the human trajectory, which is the hip joint 3D position over time and 2) the human pose which is the all other joints 3D positions over time with respect to a fixed hip joint.
DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network
We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements
LBGP: Learning Based Goal Planning for Autonomous Following in Front
Our deep RL module implicitly estimates human trajectory and produces short-term navigational goals to guide the robot.
Relational Graph Learning for Crowd Navigation
We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.
