1 citations · 1 across the 5 of their papers we have counts for
7 papers
Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds
Anastasios Manganaris, Rohit Banerjee, Gokul Prabhakaran +1
Motion planning on arbitrary Riemannian manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. In particular, motion plannin…
Composing Learned Robot Behaviors with Temporal Logic at Runtime
Moritz Zoellner, Anastasios Manganaris, Ahmed H. Qureshi +1
Executing Linear Temporal Logic (LTL) instructions with learned robot policies faces two practical challenges: demonstrations may cover individual behaviors without containing the…
Temporal Logic Guidance for Action-Only Diffusion Policies with World Models
Moritz Zoellner, Anastasios Manganaris, Rohan Paleja
Diffusion policies enable multimodal robot behavior but offer limited ability to choose among behavior modes at inference time, even though such control is desirable in human-robot…
Physics-informed Goal-Conditioned Reinforcement Learning under Hybrid Contact Dynamics
Vittorio Giammarino, Anastasios Manganaris, Ahmed H. Qureshi
Learning to reach arbitrary goals from sparse feedback requires agents to infer a rich notion of reachability across state--goal pairs. Goal-conditioned reinforcement learning (GCR…
Graph-of-Constraints Model Predictive Control for Reactive Multi-agent Task and Motion Planning
Anastasios Manganaris, Jeremy Lu, Ahmed H. Qureshi +1
Sequences of interdependent geometric constraints are central to many multi-agent Task and Motion Planning (TAMP) problems. However, existing methods for handling such constraint s…
Formal Methods in Robot Policy Learning and Verification: A Survey on Current Techniques and Future Directions
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi +1
As hardware and software systems have grown in complexity, formal methods have been indispensable tools for rigorously specifying acceptable behaviors, synthesizing programs to mee…
Automaton Constrained Q-Learning
Anastasios Manganaris, Vittorio Giammarino, Ahmed H. Qureshi
Real-world robotic tasks often require agents to achieve sequences of goals while respecting time-varying safety constraints. However, standard Reinforcement Learning (RL) paradigm…