Kostas Bekris

AAAI Conference 2026 Conference Paper

Robust Out-of-Order Retrieval for Grid-Based Storage at Maximum Capacity

• Tzvika Geft
• William Zhang
• Jingjin Yu
• Kostas Bekris

This paper proposes a framework for improving the operational efficiency of automated storage systems under uncertainty. It considers a 2D grid-based storage for uniform-sized loads (e.g., containers, pallets, or totes), which are moved by a robot (or other manipulator) along a collision-free path in the grid. The loads are labeled (i.e., unique) and must be stored in a given sequence, and later be retrieved in a different sequence---an operational pattern that arises in logistics applications, such as last-mile distribution centers and shipyards. The objective is to minimize the load relocations to ensure efficient retrieval. A previous result guarantees a zero-relocation solution for known storage and retrieval sequences, even for storage at full capacity, provided that the side of the grid through which loads are stored/retrieved is at least 3 cells wide. However, in practice, the retrieval sequence can change after the storage phase. To address such uncertainty, this work investigates k-bounded perturbations during retrieval, under which any two loads may depart out of order if they are originally at most k positions apart. We prove that a Theta(k) grid width is necessary and sufficient for eliminating relocations at maximum capacity. We also provide an efficient solver for computing a storage arrangement that is robust to such perturbations. To address the higher-uncertainty case where perturbations exceed k, a strategy is introduced to effectively minimize relocations. Extensive experiments show that, for k up to half the grid width, the proposed storage-retrieval framework essentially eliminates relocations. For k values up to the full grid width, relocations are reduced by 50%+.

AAAI Conference 2020 Conference Paper

That and There: Judging the Intent of Pointing Actions with Robotic Arms

• Malihe Alikhani
• Baber Khalid
• Rahul Shome
• Chaitanya Mitash
• Kostas Bekris
• Matthew Stone

Collaborative robotics requires effective communication between a robot and a human partner. This work proposes a set of interpretive principles for how a robotic arm can use pointing actions to communicate task information to people by extending existing models from the related literature. These principles are evaluated through studies where English-speaking human subjects view animations of simulated robots instructing pick-and-place tasks. The evaluation distinguishes two classes of pointing actions that arise in pick-and-place tasks: referential pointing (identifying objects) and locating pointing (identifying locations). The study indicates that human subjects show greater flexibility in interpreting the intent of referential pointing compared to locating pointing, which needs to be more deliberate. The results also demonstrate the effects of variation in the environment and task context on the interpretation of pointing. Our corpus, experiments and design principles advance models of context, common sense reasoning and communication in embodied communication.

AAMAS Conference 2012 Conference Paper

Maintaining Team Coherence under the Velocity Obstacle Framework

• Andrew Kimmel
• Andrew Dobson
• Kostas Bekris

Many multi-agent applications may involve a notion of spatial coherence. For instance, simulations of virtual agents often need to model a coherent group or crowd. Alternatively, robots may prefer to stay within a pre-specified communication range. This paper proposes an extension of a decentralized, reactive collision avoidance framework, which defines obstacles in the velocity space, known as Velocity Obstacles (VOs), for coherent groups of agents. The extension, referred to in this work as a Loss of Communication Obstacle (LOCO), aims to maintain proximity among agents by imposing constraints in the velocity space and restricting the set of feasible controls. If the introduction of LOCOs results in a problem that is too restrictive, then the proximity constraints are relaxed in order to maintain collision avoidance. If agents break their proximity constraints, a method is applied to reconnect them. The approach is fast and integrates nicely with the Velocity Obstacle framework. It yields improved coherence for groups of robots connected through an input constraint graph that are moving with constant velocity. Simulated environments involving a single team moving among static obstacles, as well as multiple teams operating in the same environment, are considered in the experiments and evaluated for collisions, computational cost and proximity constraint maintenance. The experiments show that improved coherence is achieved while maintaining collision avoidance, at a small computational cost and path quality degradation.

AAAI Conference 2011 Conference Paper

An Efficient and Complete Approach for Cooperative Path-Finding

• Ryan Luna
• Kostas Bekris