Pick Planning Strategies for Large-Scale Package Manipulation

• URL: http://arxiv.org/abs/2309.13224v2
• Date: Sun, 8 Oct 2023 23:22:33 GMT
• Title: Pick Planning Strategies for Large-Scale Package Manipulation
• Authors: Shuai Li, Azarakhsh Keipour, Kevin Jamieson, Nicolas Hudson, Sicong Zhao, Charles Swan and Kostas Bekris
• Abstract summary: This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet. It describes the various methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system.
• Score: 8.55680753725927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to market fluctuations. This extended abstract showcases a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet, which is used for picking and singulating up to 6 million packages per day and so far has manipulated over 2 billion packages. It describes the various heuristic methods developed over time and their successor, which utilizes a pick success predictor trained on real production data. To the best of the authors' knowledge, this work is the first large-scale deployment of learned pick quality estimation methods in a real production system.

Related papers

• Is Diversity All You Need for Scalable Robotic Manipulation? [50.747150672933316]
  We investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional intuition of "more diverse is better"<n>We show that task diversity proves more critical than per-task demonstration quantity, benefiting transfer from diverse pre-training tasks to novel downstream scenarios.<n>We propose a distribution debiasing method to mitigate velocity ambiguity, the yielding GO-1-Pro achieves substantial performance gains of 15%, equivalent to using 2.5 times pre-training data.
  arXiv  Detail & Related papers  (2025-07-08T17:52:44Z)
• Learning to Optimize Package Picking for Large-Scale, Real-World Robot Induction [17.521846970697535]
  We propose an ML-based framework that predicts transform adjustments as well as improving the selection of suction cups for sampled picks to enhance their success probabilities.<n>The proposed method achieves a 20% reduction in pick failure rates compared to a sampled-based pick sampling baseline, demonstrating its effectiveness in large-scale warehouse automation scenarios.
  arXiv  Detail & Related papers  (2025-06-11T14:04:50Z)
• Collaborative Last-Mile Delivery: A Multi-Platform Vehicle Routing Problem With En-route Charging [5.93228031688634]
  This research introduces a novel synchronized multi-platform vehicle routing problem with drones and robots.<n>A fleet of $mathcalM$ trucks, $mathcalN$ drones and $mathcalK$ robots cooperatively delivers parcels.<n>Trucks serve as mobile platforms, enabling the launching, retrieving, and en-route charging of drones and robots.
  arXiv  Detail & Related papers  (2025-05-29T15:58:01Z)
• Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services [12.270567592483888]
  We propose TransPDT, a Transformer-based multi-task package delivery time prediction model. A system based on TransPDT is deployed internally in JD Logistics to track more than 2000 couriers handling hundreds of thousands of packages per day in Beijing.
  arXiv  Detail & Related papers  (2025-05-01T08:00:22Z)
• DARS: Dynamic Action Re-Sampling to Enhance Coding Agent Performance by Adaptive Tree Traversal [55.13854171147104]
  Large Language Models (LLMs) have revolutionized various domains, including natural language processing, data analysis, and software development. We present Dynamic Action Re-Sampling (DARS), a novel inference time compute scaling approach for coding agents. We evaluate our approach on SWE-Bench Lite benchmark, demonstrating that this scaling strategy achieves a pass@k score of 55% with Claude 3.5 Sonnet V2.
  arXiv  Detail & Related papers  (2025-03-18T14:02:59Z)
• AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems [88.05152114775498]
  AgiBot World is a large-scale platform comprising over 1 million trajectories across 217 tasks in five deployment scenarios. AgiBot World guarantees high-quality and diverse data distribution. GO-1 exhibits exceptional capability in real-world dexterous and long-horizon tasks.
  arXiv  Detail & Related papers  (2025-03-09T15:40:29Z)
• Dynamic Demand Management for Parcel Lockers [0.0]
  We develop a solution framework that orchestrates algorithmic techniques rooted in Sequential Decision Analytics and Reinforcement Learning. Our innovative approach to combine these techniques enables us to address the strong interrelations between the two decision types. Our computational study shows that our method outperforms a myopic benchmark by 13.7% and an industry-inspired policy by 12.6%.
  arXiv  Detail & Related papers  (2024-09-08T11:38:48Z)
• Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search [84.39855372157616]
  This paper supports robotic parts-to-picker operations in warehousing by optimizing order-workstation assignments, item-pod assignments and the schedule of order fulfillment at workstations. We solve it via large-scale neighborhood search, with a novel learn-then-optimize approach to subproblem generation. In collaboration with Amazon Robotics, we show that our model and algorithm generate much stronger solutions for practical problems than state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-08-29T20:22:22Z)
• Meta-Evolve: Continuous Robot Evolution for One-to-many Policy Transfer [68.10957584496866]
  We propose a method that uses continuous robot evolution to efficiently transfer the policy to each target robot. The robot evolution tree allows the robot evolution paths to be shared, so our approach can significantly outperform naive one-to-one policy transfer.
  arXiv  Detail & Related papers  (2024-05-06T14:52:23Z)
• SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning [85.21378553454672]
  We develop a library containing a sample efficient off-policy deep RL method, together with methods for computing rewards and resetting the environment. We find that our implementation can achieve very efficient learning, acquiring policies for PCB board assembly, cable routing, and object relocation. These policies achieve perfect or near-perfect success rates, extreme robustness even under perturbations, and exhibit emergent robustness recovery and correction behaviors.
  arXiv  Detail & Related papers  (2024-01-29T10:01:10Z)
• DeliverAI: Reinforcement Learning Based Distributed Path-Sharing Network for Food Deliveries [1.474723404975345]
  Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.
  arXiv  Detail & Related papers  (2023-11-03T16:23:22Z)
• Analytical model for large-scale design of sidewalk delivery robot systems [4.510000677649468]
  We propose a model that captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment. We then apply the model in neighborhoods in New York City to evaluate deploying the sidewalk delivery robot system in a real-world scenario.
  arXiv  Detail & Related papers  (2023-10-26T15:26:12Z)
• Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success [12.862075198961051]
  This paper demonstrates a large-scale package manipulation from unstructured piles in Amazon Robotics' Robot Induction (Robin) fleet. The system was trained on over 394K picks and has manipulated over 200 million packages during this paper's evaluation period. The developed learned pick quality measure ranks various pick alternatives in real-time and prioritizes the most promising ones for execution.
  arXiv  Detail & Related papers  (2023-05-17T15:03:58Z)
• Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models [67.78935378952146]
  GenRL is a framework for solving sequential decision-making problems. It exploits the combination of reinforcement learning and latent variable generative models. We experimentally determine the characteristics of generative models that have most influence on the performance of the final policy training.
  arXiv  Detail & Related papers  (2022-04-18T22:02:32Z)
• CPM-2: Large-scale Cost-effective Pre-trained Language Models [71.59893315671997]
  We present a suite of cost-effective techniques for the use of PLMs to deal with the efficiency issues of pre-training, fine-tuning, and inference. We introduce knowledge inheritance to accelerate the pre-training process by exploiting existing PLMs instead of training models from scratch. We implement a new inference toolkit, namely InfMoE, for using large-scale PLMs with limited computational resources.
  arXiv  Detail & Related papers  (2021-06-20T15:43:54Z)
• Dynamic Resource Management for Providing QoS in Drone Delivery Systems [2.578242050187029]
  We study the dynamic UAV assignment problem for a drone delivery system with the goal of providing measurable Quality of Service (QoS) guarantees. We take a deep reinforcement learning approach to obtain a dynamic policy for the re-allocation of the UAVs. We evaluate the performance of our proposed algorithm by considering three broad arrival classes, including Bernoulli, Time-Varying Bernoulli, and Markov-Modulated Bernoulli arrivals.
  arXiv  Detail & Related papers  (2021-03-06T03:11:07Z)
• Improving Auto-Augment via Augmentation-Wise Weight Sharing [123.71986174280741]
  A key component of automatic augmentation search is the evaluation process for a particular augmentation policy. In this paper, we dive into the dynamics of augmented training of the model. We design a powerful and efficient proxy task based on the Augmentation-Wise Weight Sharing (AWS) to form a fast yet accurate evaluation process.
  arXiv  Detail & Related papers  (2020-09-30T15:23:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.