Walter Jansma
Papers
1
Total Citations
4
H-Index
1
About
Walter Jansma is a roboticist advancing autonomous navigation in complex, human-shared environments. His research centers on motion planning and control, with a particular focus on interaction-aware decision-making for robots operating in tight, mixed human-robot spaces. Jansma’s key contribution is the development of sampling-based Model Predictive Control (MPC) frameworks that integrate learned local goal predictions, enabling robots to anticipate and respond to the intentions of other agents in real time. This approach bridges the gap between prediction and planning—a longstanding challenge in the field—by allowing the robot to reason about interactions during the planning process rather than treating them as separate steps. His most-cited work, "Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions" (2023), has already garnered 4 citations, reflecting its timely relevance. Jansma’s work is notable for its practical impact on safe, efficient robot navigation in crowded settings, from warehouses to public spaces, and positions him as an emerging voice in the robotics community.
Research Focus
Key Achievements
Top Papers
- 1Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions4 citations · 2023