TANUSH PAVAN VAKKALAGADDA@tanush_pavan_v385
2
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India
1 year of experience
About me
I am an Electrical and Electronics Engineering student at Amrita Vishwa Vidyapeetham, concurrently pursuing a B.S. in Data Science and Applications from IIT Madras. My primary focus lies at the intersection of digital VLSI design, microarchitecture development, hardware acceleration, and embedded systems. I specialize in bridging hardware and software, with hands-on experience designing RTL-based architectures, building automated verification frameworks for RISC-V processors, and deploying deep learning models onto FPGA platforms like the AMD Versal VCK190. Alongside hardware design, I develop end-to-end machine learning pipelines, cloud architectures, and agentic workflows—combining high-performance compute design with modern AI systems.
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Bimanual Dinner-Table AI on Intel Edge
We built an end-to-end Physical AI system in which two simulated SO-101 robot arms set a dinner table from a single natural-language command in MuJoCo. A planner turns the sentence into a two-arm plan: open the drawer, fetch the spoon and fork, place the plate, hand the fork from arm B to arm A, and pour from a bottle into a mug that the other arm holds steady by its handle. A closed-loop executor carries out each step, checks the result against the actual scene, and retries or re-plans when something fails or a finished goal gets undone, such as the drawer being bumped shut. Every seed randomizes object positions, masses, friction, sizes, mug shape and lighting. With scripted bimanual skills, the full task succeeds on 10/10 evaluation seeds and 7/10 held-out seeds. For learning, we collected 300 expert episodes (117k frames) and trained ACT-Lite, a 1.6M-parameter language-conditioned action-chunking transformer that uses two cameras, joint state and the sub-instruction. When it handles the plate and mug picks, the full task succeeds on 5/10 seeds, and it completes 80% of its picks without help. The policy is exported to OpenVINO IR in FP32, FP16 and INT8 (NNCF quantization). On an Intel i9-13900HX, INT8 inference takes 1.35 ms, 5.2x faster than PyTorch, with 3,400 inferences per second in throughput mode; the Intel iGPU runs it in 2.56 ms. Simulation, perception and policy together run faster than real time. The repo includes the MuJoCo scene, data collection, training, export, benchmark and 10-seed evaluation scripts, plus a video of all seeds. Limitations are stated openly: grasping uses a weld-based assist, pouring is judged geometrically, and there are no NPU numbers because the test laptop is not a Core Ultra system.
16 Sep 2026
