Author name cluster

Kowndinya Boyalakuntla

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3 papers

1 author row

IROS Conference 2025 Conference Paper

KARL: Kalman-Filter Assisted Reinforcement Learner for Dynamic Object Tracking and Grasping

Kowndinya Boyalakuntla
Abdeslam Boularias
Jingjin Yu

We present Kalman-Filter Assisted Reinforcement Learner (KARL) for dynamic object tracking and grasping over eye-on-hand (EoH) systems, significantly expanding such systems’ capabilities in challenging, realistic environments. In comparison to the previous state-of-the-art, KARL (1) incorporates a novel six-stage RL curriculum that doubles the system’s motion range, thereby greatly enhancing the system’s grasping performance, (2) integrates a robust Kalman filter layer between the perception and reinforcement learning (RL) control modules, enabling the system to maintain an uncertain but continuous 6D pose estimate even when the target object temporarily exits the camera’s field-of-view or undergoes rapid, unpredictable motion, and (3) introduces mechanisms to allow retries to gracefully recover from unavoidable policy execution failures. Extensive evaluations conducted in both simulation and real-world experiments qualitatively and quantitatively corroborate KARL’s advantage over earlier systems, achieving higher grasp success rates and faster robot execution speed. Source code and supplementary materials for KARL will be made available at: https://github.com/arc-l/karl.

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IROS Conference 2024 Conference Paper

DAP: Diffusion-based Affordance Prediction for Multi-modality Storage

Haonan Chang
Kowndinya Boyalakuntla
Yuhan Liu
Xinyu Zhang
Liam Schramm
Abdeslam Boularias

Solving storage problems—where objects must be accurately placed into containers with precise orientations and positions—presents a distinct challenge that extends beyond traditional rearrangement tasks. These challenges are primarily due to the need for fine-grained 6D manipulation and the inherent multi-modality of solution spaces, where multiple viable goal configurations exist for the same storage container. We present a novel Diffusion-based Affordance Prediction (DAP) pipeline for the multi-modal object storage problem. DAP leverages a two-step approach, initially identifying a placeable region on the container and then precisely computing the relative pose between the object and that region. Existing methods either struggle with multi-modality issues or computation-intensive training. Our experiments demonstrate DAP’s superior performance and training efficiency over the current state-of-the-art RPDiff, achieving remarkable results on the RPDiff benchmark. Additionally, our experiments showcase DAP’s data efficiency in real-world applications, an advancement over existing simulation-driven approaches. Our contribution fills a gap in robotic manipulation research by offering a solution that is both computationally efficient and capable of handling real-world variability. Code and supplementary material can be found at: https://github.com/changhaonan/DPS.git.

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IROS Conference 2024 Conference Paper

LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement

Haonan Chang
Kai Gao
Kowndinya Boyalakuntla
Alex Lee
Baichuan Huang
Jingjin Yu
Abdeslam Boularias

We present LGMCTS, a framework that uniquely combines language guidance with geometrically informed sampling distributions to effectively rearrange objects according to geometric patterns dictated by natural language descriptions. LGMCTS uses Monte Carlo Tree Search (MCTS) to create feasible action plans that ensure executable semantic object rearrangement. We present a comprehensive comparison with leading approaches that use language to generate goal rearrangements independently of actionable planning, including Structformer, StructDiffusion, and Code as policies. We also present a new benchmark, the Executable Language Guided Rearrangement (ELGR) Bench, containing tasks involving intricate geometry. With the ELGR bench, we show limitations of task and motion planning (TAMP) solutions that are purely based on Large Language Models (LLM) such as Code as Policies and Progprompt on such tasks. Our findings advocate for using LLMs to generate intermediary representations rather than direct action planning in geometrically complex rearrangement scenarios, aligning with perspectives from recent literature. Our code and supplementary materials are accessible at https://lgmcts.github.io/.

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