Competition in warehouse automation continues to intensify, with new capabilities emerging for robots tasked with handling complex items in challenging fulfillment environments. Black-I Robotics has recently drawn industry attention after clinching the $30,000 first-place prize in the Chewy Autonomous Mobile Picking (CHAMP) Challenge, an event jointly organized by Chewy and MassRobotics. The winning solution centered on a robotic system capable of manipulating large, heavy, and deformable products often encountered in e-commerce warehouses. Efforts to advance such technology reflect broader trends as logistics companies seek to address persistent gaps in fully autonomous material handling for diverse product lines.
Similar robotics challenges in recent years have also targeted manipulation of non-uniform and hard-to-grip objects, but past events primarily prioritized picking speed or isolated grasping performance. Unlike these earlier competitions, the CHAMP Challenge set its scope to include navigation through narrow aisles, seamless integration with existing workflows, and the ability to handle real-world operational demands. This more holistic approach placed emphasis not just on picking items, but on effective system-level deployment in active warehouse settings. Recent coverage indicates that prior finalists in comparable events encountered difficulty moving from demonstration to real-life fulfillment scenarios.
How Did Teams Address Warehouse Complexity?
Participants were tasked with developing autonomous systems that could operate reliably in the demanding and congested environments typical of modern fulfillment centers. The challenge required that their robots efficiently pick unusually shaped and heavy items, which often exceed 40 pounds and feature irregular surfaces. Competing teams collaborated with Chewy’s technical staff, gaining insights into practical constraints and operational workflows—ranging from real-time pallet identification to item placement in mixed product bins.
What Set Black-I Robotics Apart?
Black-I Robotics achieved distinction by introducing a full-stack system that leverages a mobile robotic base fitted with a six-degree-of-freedom industrial manipulator. Their design incorporated a custom multi-modal end effector tailored for adaptable gripping, supported by AI-driven perception software for precise object detection and manipulation—even when products were loosely stacked or lacked rigid forms.
“The ability to dynamically recognize objects and plan secure grasps in less-structured settings proved crucial to the robot’s performance,”
said one expert involved in evaluating the competition.
Breezey Machine Company Delivers an Adaptable Solution
Securing second place and a $15,000 award, the Breezey Machine Company focused on mechanical innovation. Their entry featured a low-profile, compliant gripper designed to secure deformable items with minimal reliance on complex vision systems or extensive calibration. Additionally, the team proposed a modular platform capable of being retrofitted onto legacy robots or deployed within contained robotic cells—prioritizing flexibility and integration into existing warehouse infrastructure. Judges recognized the practicality and scalability of Breezey’s approach, particularly its potential as a subsystem in broader automation strategies.
By facilitating collaboration between robotics startups and seasoned engineers, the CHAMP Challenge has highlighted the multifaceted challenges of automating large-item picking in fulfillment centers. Black-I Robotics demonstrated a robust, facility-wide navigation system using fiducial markers and SLAM, enabling the robot to avoid obstacles and autonomously position selected items in diverse shipping containers. Breezey Machine Company’s design, in contrast, excelled in adaptability and manufacturing feasibility, addressing real-world needs for scalable upgrades in automation deployments.
Developers and warehouse operators can draw several lessons from this event: integrating AI with traditional robotics expands capabilities for handling non-standard products, and adaptability—as seen in Breezey’s modular design—can accelerate adoption by enabling legacy system upgrades. As these solutions aim for real-world reliability, their progress is likely to influence future standards for warehouse robotics. Careful attention to both technical execution and practical constraints remains necessary as industry stakeholders continue to narrow the gap between innovative prototypes and daily fulfillment center operation.
