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Avanishika Revelli
Junior Software Engineer specializing in backend, cloud, and robotics automation
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Graduate Research Assistant in Robotics at Arizona State University who built an end-to-end LLM-driven task execution framework enabling collaborative robots to convert high-level natural language instructions into safe, executable ROS actions. Implemented robust monitoring, failure detection, and automatic replanning, and addressed real-world issues like timestamp/frame-transform mismatches and heterogeneous robot interoperability using adapter nodes.
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Key Strengths
- End-to-end design and implementation of an LLM-driven robot task execution framework
- Translating LLM-generated plans into executable ROS actions/services (pick-and-place, navigation)
- Robust real-time monitoring using action feedback/results with clear failure criteria
- Debugging motion planning failures via timestamp synchronization and correct frame transforms
- Building interoperability layers (adapter nodes) across heterogeneous robots with time alignment and buffering
- Effective use of Gazebo simulation to validate planning/collision avoidance before hardware deployment
- Containerization and scalable deployment using Docker and Kubernetes with Jenkins-based CI/CD
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