- Interests:
- High-quality plans that
- Respect of physical constraints of the system
- Real time constraints are important
- Real-time planning may require short planning horizons which can lead to situations where collision cannot be avoided
- Multi-agent planning
- Interaction with humans
- Methods cited for control theory: LQR, partial feedback linearization, HJB and pnotyagin’s principle
- In late 70s, PSPACE-Hardness was established (Reif ’79, Schwartz & Sharir 82, 84)
- Cites Canny’s work (I just grabbed his thesis from the library), talked about roadmaps, but the algorithm presented was doubly-exponential, never even implemented.
- PRMs were introduced by Kavarki, Bekris’ advisor in 1996. An efficient way of producing roadmaps
- In the general case, planning without respecting dynamics and then trying to smooth the path so it complies with dynamics is not possible
- He is interested in finding more computationally near-optimal results as opposed to optimal results at the limit with infinitely dense graphs
- Do dense planning and then prune
- His general goal is PAC-style planning
- The results in the field are almost entirely at the limit
- Under differential constraints, PRMs are difficult because of the BVP problem, RRTs dont have that problem
- Methods of encouraging efficient exploration (otherwise acrobot for example just hangs down with random actions)
- Can do replanning in domains that are non-static
- Multi-agent path planning is poly-time (non-optimal) in discrete domains
- Future work in area of co-robotics (robot, human interaction), maybe from perspective of game theory, but want pareto optimality, introduces a number of other problems, of course
- They may get a BAXTER(!)
- Minimax regret: Savage ’51, Niehas ’48
- Good for human interaction, because minimax results agree more with human intuition than game-theoretic results