This paper presents a collaborative reinforcement learning algorithm, CQ(λ), designed to accelerate learning by integrating a human operator into the learning process. The CQ(λ)-learning algorithm enables collaboration of knowledge between the robot and a human; the human, responsible for remotely monitoring the robot, suggests solutions when intervention is required. Based on its learning performance, the robot switches between fully autonomous operation, and the integration of human commands. The CQ(λ) -learning algorithm was tested on a Motoman UP-6 fixed-arm robot required to empty the contents of a suspicious bag. Experimental results of comparing the CQ(λ) with the standard Q(λ) , indicated the superiority of the CQ(λ) while achieving an improvement of 21.25% in the average reward.