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We introduce OPEND, a benchmark for learning how to use a hand to opencabinet doors or drawers in a photo-realistic and physics-reliable simulationenvironment driven by language instruction. To solve the task, we propose amulti-step planner composed of a deep neural network and rule-base controllers.The network is utilized to capture spatial relationships from images andunderstand semantic meaning from language instructions. Controllers efficientlyexecute the plan based on the spatial and semantic understanding. We evaluateour system by measuring its zero-shot performance in test data set.Experimental results demonstrate the effectiveness of decision planning by ourmulti-step planner for different hands, while suggesting that there issignificant room for developing better models to address the challenge broughtby language understanding, spatial reasoning, and long-term manipulation. Wewill release OPEND and host challenges to promote future research in this area.

OpenD: A Benchmark for Language-Driven Door and Drawer Opening